+
+Foundation machine learning interatomic potentials (MLIPs), trained on extensive databases containing millions of density functional theory (DFT) calculations, have revolutionized molecular and materials modeling, but existing benchmarks suffer from data leakage, limited transferability, and an over-reliance on error-based metrics tied to specific density functional theory (DFT) references.
+
+We introduce MLIP Arena, a unified benchmark platform for evaluating foundation MLIP performance beyond conventional error metrics. It focuses on revealing the physical soundness learned by MLIPs and assessing their utilitarian performance agnostic to underlying model architecture and training dataset.
+
+***By moving beyond static DFT references and revealing the important failure modes*** of current foundation MLIPs in real-world settings, MLIP Arena provides a reproducible framework to guide the next-generation MLIP development toward improved predictive accuracy and runtime efficiency while maintaining physical consistency.
+
+MLIP Arena leverages modern pythonic workflow orchestrator ๐
+ [Prefect](https://www.prefect.io/) ๐
+ to enable advanced task/flow chaining and caching.
+
+
+
+> [!NOTE]
+> Contributions of new tasks through PRs are very welcome! If you're interested in joining the effort, please reach out to Yuan at [cyrusyc@berkeley.edu](mailto:cyrusyc@berkeley.edu). See [project page](https://github.com/orgs/atomind-ai/projects/1) for some outstanding tasks, or propose new feature requests in [Discussion](https://github.com/atomind-ai/mlip-arena/discussions/new?category=ideas).
+
+## Announcement
+
+- **[April 8, 2025]** [๐ **MLIP Arena is accepted as an ICLR AI4Mat Spotlight!** ๐](https://openreview.net/forum?id=ysKfIavYQE#discussion) Huge thanks to all co-authors for their contributions!
+
+
+## Installation
+
+### From PyPI (prefect workflow only, without pretrained models)
+
+```bash
+pip install mlip-arena
+```
+
+### From source (with integrated pretrained models, advanced)
+
+> [!CAUTION]
+> We strongly recommend clean build in a new virtual environment due to the compatibility issues between multiple popular MLIPs. We provide a single installation script using `uv` for minimal package conflicts and fast installation!
+
+> [!CAUTION]
+> To automatically download farichem OMat24 checkpoint, please make sure you have gained downloading access to their HuggingFace [***model repo***](https://huggingface.co/facebook/OMAT24) (not dataset repo), and login locally on your machine through `huggginface-cli login` (see [HF hub authentication](https://huggingface.co/docs/huggingface_hub/en/quick-start#authentication))
+
+**Linux**
+
+```bash
+# (Optional) Install uv, way faster than pip, why not? :)
+curl -LsSf https://astral.sh/uv/install.sh | sh
+source $HOME/.local/bin/env
+
+git clone https://github.com/atomind-ai/mlip-arena.git
+cd mlip-arena
+
+# One script uv pip installation
+bash scripts/install.sh
+```
+
+> [!TIP]
+> Sometimes installing all compiled models takes all the available local storage. Optional pip flag `--no-cache` could be uesed. `uv cache clean` will be helpful too.
+
+**Mac**
+
+```bash
+# (Optional) Install uv
+curl -LsSf https://astral.sh/uv/install.sh | sh
+source $HOME/.local/bin/env
+# One script uv pip installation
+bash scripts/install-macosx.sh
+```
+
+## Quickstart
+
+### The first example: Molecular Dynamics
+
+Arena provides a unified interface to run all the compiled MLIPs. This can be achieved simply by looping through `MLIPEnum`:
+
+```python
+from mlip_arena.models import MLIPEnum
+from mlip_arena.tasks import MD
+from mlip_arena.tasks.utils import get_calculator
+
+from ase import units
+from ase.build import bulk
+
+atoms = bulk("Cu", "fcc", a=3.6) * (5, 5, 5)
+
+results = []
+
+for model in MLIPEnum:
+ result = MD(
+ atoms=atoms,
+ calculator=get_calculator(
+ model,
+ calculator_kwargs=dict(), # passing into calculator
+ dispersion=True,
+ dispersion_kwargs=dict(
+ damping='bj', xc='pbe', cutoff=40.0 * units.Bohr
+ ), # passing into TorchDFTD3Calculator
+ ), # compatible with custom ASE Calculator
+ ensemble="nve", # nvt, nvt available
+ dynamics="velocityverlet", # compatible with any ASE Dynamics objects and their class names
+ total_time=1e3, # 1 ps = 1e3 fs
+ time_step=2, # fs
+ )
+ results.append(result)
+```
+
+### ๐ Parallelize Benchmarks at Scale
+
+To run multiple benchmarks in parallel, add `.submit` before the task function and wrap all the tasks into a flow to dispatch the tasks to worker for concurrent execution. See Prefect Doc on [tasks](https://docs.prefect.io/v3/develop/write-tasks) and [flow](https://docs.prefect.io/v3/develop/write-flows) for more details.
+
+```python
+...
+from prefect import flow
+
+@flow
+def run_all_tasks:
+
+ futures = []
+ for model in MLIPEnum:
+ future = MD.submit(
+ atoms=atoms,
+ ...
+ )
+ future.append(future)
+
+ return [f.result(raise_on_failure=False) for f in futures]
+```
+
+For a more practical example using HPC resources, please now refer to [MD stability benchmark](../benchmarks/stability/temperature.ipynb).
+
+### List of implemented tasks
+
+The implemented tasks are available under `mlip_arena.tasks..run` or `from mlip_arena.tasks import *` for convenient imports (currently doesn't work if [phonopy](https://phonopy.github.io/phonopy/install.html) is not installed).
+
+- [OPT](../mlip_arena/tasks/optimize.py#L56): Structure optimization
+- [EOS](../mlip_arena/tasks/eos.py#L42): Equation of state (energy-volume scan)
+- [MD](../mlip_arena/tasks/md.py#L200): Molecular dynamics with flexible dynamics (NVE, NVT, NPT) and temperature/pressure scheduling (annealing, shearing, *etc*)
+- [PHONON](../mlip_arena/tasks/phonon.py#L110): Phonon calculation driven by [phonopy](https://phonopy.github.io/phonopy/install.html)
+- [NEB](../mlip_arena/tasks/neb.py#L96): Nudged elastic band
+- [NEB_FROM_ENDPOINTS](../mlip_arena/tasks/neb.py#L164): Nudge elastic band with convenient image interpolation (linear or IDPP)
+- [ELASTICITY](../mlip_arena/tasks/elasticity.py#L78): Elastic tensor calculation
+
+### Contribute and Development
+
+PRs are welcome. Please clone the repo and submit PRs with changes.
+
+To make change to huggingface space, fetch large files from git lfs first and run streamlit:
+
+```
+git lfs fetch --all
+git lfs pull
+streamlit run serve/app.py
+```
+
+### Add new benchmark tasks (WIP)
+
+> [!NOTE]
+> Please reuse, extend, or chain the general tasks defined [above](#list-of-implemented-tasks)
+
+### Add new MLIP models
+
+If you have pretrained MLIP models that you would like to contribute to the MLIP Arena and show benchmark in real-time, there are two ways:
+
+#### External ASE Calculator (easy)
+
+1. Implement new ASE Calculator class in [mlip_arena/models/externals](../mlip_arena/models/externals).
+2. Name your class with awesome model name and add the same name to [registry](../mlip_arena/models/registry.yaml) with metadata.
+
+> [!CAUTION]
+> Remove unneccessary outputs under `results` class attributes to avoid error for MD simulations. Please refer to [CHGNet](../mlip_arena/models/externals/chgnet.py) as an example.
+
+#### Hugging Face Model (recommended, difficult)
+
+0. Inherit Hugging Face [ModelHubMixin](https://huggingface.co/docs/huggingface_hub/en/package_reference/mixins) class to your awesome model class definition. We recommend [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/en/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin).
+1. Create a new [Hugging Face Model](https://huggingface.co/new) repository and upload the model file using [push_to_hub function](https://huggingface.co/docs/huggingface_hub/en/package_reference/mixins#huggingface_hub.ModelHubMixin.push_to_hub).
+2. Follow the template to code the I/O interface for your model [here](../mlip_arena/models/README.md).
+3. Update model [registry](../mlip_arena/models/registry.yaml) with metadata
+
+## Citation
+
+If you find the work useful, please consider citing the following:
+
+```bibtex
+@inproceedings{
+ chiang2025mlip,
+ title={{MLIP} Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials through an Open and Accessible Benchmark Platform},
+ author={Yuan Chiang and Tobias Kreiman and Elizabeth Weaver and Ishan Amin and Matthew Kuner and Christine Zhang and Aaron Kaplan and Daryl Chrzan and Samuel M Blau and Aditi S. Krishnapriyan and Mark Asta},
+ booktitle={AI for Accelerated Materials Design - ICLR 2025},
+ year={2025},
+ url={https://openreview.net/forum?id=ysKfIavYQE}
+}
+```
\ No newline at end of file
diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5e00811148edf490168655ae3c1f1e743095b1d0
--- /dev/null
+++ b/.github/workflows/release.yaml
@@ -0,0 +1,96 @@
+name: Publish Release
+
+on:
+ workflow_dispatch:
+
+permissions:
+ contents: write # Ensure write access to push tags
+
+jobs:
+ pypi:
+ name: Publish to PyPI
+ runs-on: ubuntu-latest
+
+ steps:
+ # Step 1: Checkout the code
+ - name: Checkout code
+ uses: actions/checkout@v3
+
+ # Step 2: Set up Python
+ - name: Set up Python
+ uses: actions/setup-python@v4
+ with:
+ python-version: '3.x'
+
+ # Step 3: Install dependencies
+ - name: Install dependencies
+ run: pip install toml requests
+
+ # Step 4: Extract current version from pyproject.toml
+ - name: Extract current version
+ id: get_version
+ run: |
+ VERSION=$(python -c "import toml; print(toml.load('pyproject.toml')['project']['version'])")
+ echo "VERSION=$VERSION" >> $GITHUB_ENV
+
+ # Step 5: Get latest version from PyPI
+ - name: Get latest version from PyPI
+ id: get_pypi_version
+ run: |
+ LATEST_PYPI_VERSION=$(python -c "import toml; import requests; PACKAGE_NAME = toml.load('pyproject.toml')['project']['name']; response = requests.get(f'https://pypi.org/pypi/{PACKAGE_NAME}/json'); print(response.json()['info']['version'])")
+ echo "LATEST_PYPI_VERSION=$LATEST_PYPI_VERSION" >> $GITHUB_ENV
+
+ # Step 6: Compare current version with the latest tag
+ - name: Check if version is bumped
+ id: check_version
+ run: |
+ if [ "${{ env.VERSION }}" = "${{ env.LATEST_PYPI_VERSION }}" ]; then
+ echo "Version not bumped. Exiting."
+ echo "version_bumped=false" >> $GITHUB_ENV
+ else
+ echo "Version bumped. Proceeding."
+ echo "version_bumped=true" >> $GITHUB_ENV
+ fi
+
+ # Step 5: Remove problematic optional dependencies
+ - name: Strip problematic optional dependencies
+ run: |
+ python - < ~/.pypirc
+ echo "username = __token__" >> ~/.pypirc
+ echo "password = ${{ secrets.PYPI_API_TOKEN }}" >> ~/.pypirc
+
+ # Step 9: Build and publish package (only if version bumped)
+ - name: Build and Publish Package
+ if: env.version_bumped == 'true'
+ run: |
+ flit build
+ flit publish
diff --git a/.github/workflows/sync-hf.yaml b/.github/workflows/sync-hf.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8bb8ea7602899d586ba2cd426f3a41e002dbc12c
--- /dev/null
+++ b/.github/workflows/sync-hf.yaml
@@ -0,0 +1,39 @@
+name: Sync to Hugging Face hub
+
+on:
+ workflow_run:
+ workflows: [Python Test]
+ branches: [main]
+ types: [completed]
+ workflow_dispatch:
+
+jobs:
+ sync-to-hub:
+ if: ${{ github.event.workflow_run.conclusion == 'success' }}
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ with:
+ fetch-depth: 0
+ lfs: true
+
+ - name: Push to hub
+ env:
+ HF_TOKEN: ${{ secrets.HF_TOKEN }}
+ run: |
+ # Configure Git user identity
+ git config user.name "github-actions[ci]"
+ git config user.email "github-actions[ci]@users.noreply.github.com"
+
+ # Configure LFS tracking
+ git lfs track "*.pdf"
+ git lfs track "*.png"
+
+ # Create a new orphan branch (no history)
+ git checkout --orphan hf-clean
+
+ git add .
+ git commit -m "Clean sync from main branch - $(date '+%Y-%m-%d %H:%M:%S')"
+
+ # Force push to Hugging Face main branch
+ git push -f https://HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/atomind/mlip-arena hf-clean:main
\ No newline at end of file
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..753bed9074968eaa70f79288d3b2ca8232c960c3
--- /dev/null
+++ b/.github/workflows/test.yaml
@@ -0,0 +1,103 @@
+name: Python Test
+
+on:
+ push:
+ branches: [main]
+ pull_request:
+ branches: [main]
+
+env:
+ UV_SYSTEM_PYTHON: 1
+
+jobs:
+ test:
+ runs-on: ubuntu-latest
+
+ strategy:
+ matrix:
+ python-version: ["3.10", "3.11", "3.12"]
+
+ steps:
+ - name: Checkout PR with full history
+ uses: actions/checkout@v4
+ with:
+ fetch-depth: 0
+
+ - name: Install uv
+ uses: astral-sh/setup-uv@v6
+ with:
+ enable-cache: true
+ cache-dependency-glob: "pyproject.toml"
+
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v5
+ with:
+ python-version: ${{ matrix.python-version }}
+
+ - name: Install dependencies
+ run: bash scripts/install-linux.sh
+
+ - name: List dependencies
+ run: pip list
+
+ - name: Login to Hugging Face
+ env:
+ HF_TOKEN: ${{ secrets.HF_TOKEN_READ_ONLY }}
+ run: huggingface-cli login --token $HF_TOKEN
+
+ - name: Run tests
+ env:
+ PREFECT_API_KEY: ${{ secrets.PREFECT_API_KEY }}
+ PREFECT_API_URL: ${{ secrets.PREFECT_API_URL }}
+ run: pytest -vra -n 5 --dist=loadscope tests
+
+ - name: Squash commits and trial push to Hugging Face
+ if: github.event_name == 'pull_request'
+ id: trial_push
+ env:
+ HF_TOKEN: ${{ secrets.HF_TOKEN }}
+ TRIAL_BRANCH: trial-sync-${{ github.sha }}-${{ matrix.python-version }}
+ run: |
+ # Configure Git user identity
+ git config user.name "github-actions[ci]"
+ git config user.email "github-actions[ci]@users.noreply.github.com"
+
+ # Install Git LFS
+ sudo apt-get update
+ sudo apt-get install -y git-lfs
+ git lfs install
+
+ # Configure LFS tracking for binary files (only for HF push)
+ git lfs track "*.pdf"
+ git lfs track "*.png"
+
+ git add .gitattributes
+
+ # Setup LFS for the remote
+ git lfs fetch
+ git lfs checkout
+
+ # Rebase and squash all PR commits into one
+ BASE=$(git merge-base origin/main HEAD)
+ git reset --soft $BASE
+
+ # Re-add all files (binary files will now be tracked by LFS)
+ git add .
+ git commit -m "Squashed commit from PR #${{ github.event.pull_request.number }}"
+
+ # Create a new orphan branch (no history)
+ git checkout --orphan hf-clean
+
+ git add .
+ git commit -m "Clean sync from main branch - $(date '+%Y-%m-%d %H:%M:%S')"
+
+ # Push to temporary branch on Hugging Face
+ git push -f https://HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/atomind/mlip-arena HEAD:refs/heads/$TRIAL_BRANCH
+
+ - name: Delete trial branch from Hugging Face
+ if: steps.trial_push.outcome == 'success'
+ env:
+ HF_TOKEN: ${{ secrets.HF_TOKEN }}
+ TRIAL_BRANCH: trial-sync-${{ github.sha }}-${{ matrix.python-version }}
+ run: |
+ git push https://HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/atomind/mlip-arena --delete $TRIAL_BRANCH || true
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..125b29977c7d1fcd4b66fb3556b90829ff0e99b8
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,169 @@
+*.out
+*.extxyz
+*.traj
+mlip_arena/tasks/*/
+benchmarks/
+lab/
+manuscripts/
+datasets/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+# in version control.
+# https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+# and can be added to the global gitignore or merged into this file. For a more nuclear
+# option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
diff --git a/.streamlit/config.toml b/.streamlit/config.toml
new file mode 100644
index 0000000000000000000000000000000000000000..f5549d1fcd6363da43c2b96fa5e9394fea789521
--- /dev/null
+++ b/.streamlit/config.toml
@@ -0,0 +1,2 @@
+[server]
+fileWatcherType = "poll"
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000000000000000000000000000000000000..c09003015af2ccbf8600665792c0df85d241bff1
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,23 @@
+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
+cff-version: 1.2.0
+title: MLIP Arena
+message: >-
+ If you use this software, please cite it using the
+ metadata from this file.
+type: software
+authors:
+ - given-names: Yuan
+ family-names: Chiang
+ email: cyrusyc@lbl.gov
+ affiliation: Lawrence Berkeley National Laboratory
+ orcid: 'https://orcid.org/0000-0002-4017-7084'
+repository-code: 'https://github.com/atomind-ai/mlip-arena'
+keywords:
+ - Quantum Chemistry
+ - Foundation Model
+ - Interatomic Potentials
+ - Machine Learning
+ - Force Fields
+license: Apache-2.0
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..63bc1facf120b8b3ae45509f6aa866e9b136b287
--- /dev/null
+++ b/README.md
@@ -0,0 +1,14 @@
+---
+title: MLIP Arena
+emoji: โ
+sdk: streamlit
+sdk_version: 1.43.2 # The latest supported version
+python_version: 3.11
+app_file: serve/app.py
+colorFrom: indigo
+colorTo: yellow
+pinned: true
+short_description: Benchmark machine learning interatomic potential at scale
+---
+
+
diff --git a/benchmarks/bzo/dft.ipynb b/benchmarks/bzo/dft.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..628cae23a8b19aa4d1f2f6308e3762884df9cb5a
--- /dev/null
+++ b/benchmarks/bzo/dft.ipynb
@@ -0,0 +1,340 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/torchani/aev.py:16: UserWarning: cuaev not installed\n",
+ " warnings.warn(\"cuaev not installed\")\n",
+ "\u001b[32m2025-03-18 22:33:32.183\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mmlip_arena.models\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m34\u001b[0m - \u001b[33m\u001b[1mNo module named 'deepmd'\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from mlip_arena.tasks import MD, PHONON, OPT\n",
+ "from mlip_arena.tasks.utils import get_calculator\n",
+ "from ase.build import bulk\n",
+ "from ase.io import read\n",
+ "import numpy as np\n",
+ "\n",
+ "atoms = read('BZO_cubic_prim.xyz')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
22:33:35.208 | INFO | prefect - Starting temporary server on http://127.0.0.1:8224\n",
+ "See https://docs.prefect.io/3.0/manage/self-host#self-host-a-prefect-server for more information on running a dedicated Prefect server.\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "22:33:35.208 | \u001b[36mINFO\u001b[0m | prefect - Starting temporary server on \u001b[94mhttp://127.0.0.1:8224\u001b[0m\n",
+ "See \u001b[94mhttps://docs.prefect.io/3.0/manage/self-host#self-host-a-prefect-server\u001b[0m for more information on running a dedicated Prefect server.\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "
22:33:41.734 | INFO | Task run 'OPT: BaO3Zr - <ase.calculators.vasp.vasp.Vasp object at 0x7f582ccbee10>' - Finished in state Cached(type=COMPLETED)\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import plotly.express as px\n",
+ "\n",
+ "for i, method in enumerate(methods):\n",
+ " filtered_data = data[data['method'] == method]\n",
+ " filtered_data = filtered_data.drop_duplicates(subset=['method', 'formula', 'volume'], keep='last')\n",
+ " \n",
+ " fig = px.scatter_ternary(\n",
+ " filtered_data, a=\"Fe\", b=\"Ni\", c=\"Cr\", color=\"b0\", \n",
+ " )\n",
+ " \n",
+ " fig.update_layout(title=method)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "mlip-arena",
+ "language": "python",
+ "name": "mlip-arena"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "state": {},
+ "version_major": 2,
+ "version_minor": 0
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/benchmarks/eos_bulk/CHGNet.parquet b/benchmarks/eos_bulk/CHGNet.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..43dad7c081ddbc284dc6e6b2c2852465debfa320
--- /dev/null
+++ b/benchmarks/eos_bulk/CHGNet.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68871d694e93a3c3e7e272b9cbd87d3757e3bc689f30f3189db232d76e629c07
+size 429910
diff --git a/benchmarks/eos_bulk/CHGNet_processed.parquet b/benchmarks/eos_bulk/CHGNet_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..a942f3448c1013017ba0bb8a7aa85fa23f9345cd
--- /dev/null
+++ b/benchmarks/eos_bulk/CHGNet_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bfde7530e6b0d2df5a30e1b7e3ec124fb2a86f6da8e35d2548d37d10a1eff1b1
+size 387425
diff --git a/benchmarks/eos_bulk/M3GNet.parquet b/benchmarks/eos_bulk/M3GNet.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..0ce0cb0cd28f9e7203404427fe68d6b04edb859c
--- /dev/null
+++ b/benchmarks/eos_bulk/M3GNet.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:53dde465b5e10edd677f131f8a531e3dfc36303dd7ec7b9df0060c19847494d9
+size 427419
diff --git a/benchmarks/eos_bulk/M3GNet_processed.parquet b/benchmarks/eos_bulk/M3GNet_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..d1b291c5a1b157174c53acde829d6da0b08de8a0
--- /dev/null
+++ b/benchmarks/eos_bulk/M3GNet_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 397450
diff --git a/benchmarks/eos_bulk/MACE-MP(M).parquet b/benchmarks/eos_bulk/MACE-MP(M).parquet
new file mode 100644
index 0000000000000000000000000000000000000000..7287426ae925a64ec1400a9f641848be301ada49
--- /dev/null
+++ b/benchmarks/eos_bulk/MACE-MP(M).parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff9769eeb83042129767aeff975eb04dee8efae12e96fbd46cd3039eeda26705
+size 427896
diff --git a/benchmarks/eos_bulk/MACE-MP(M)_processed.parquet b/benchmarks/eos_bulk/MACE-MP(M)_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..e681cec5643003bfea9db29799c19cab5f26b9a5
--- /dev/null
+++ b/benchmarks/eos_bulk/MACE-MP(M)_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/benchmarks/eos_bulk/MACE-MPA.parquet b/benchmarks/eos_bulk/MACE-MPA.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..ae18b9d947a17edb51ce0fecd2e6cc066d74d0a5
--- /dev/null
+++ b/benchmarks/eos_bulk/MACE-MPA.parquet
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+size 428888
diff --git a/benchmarks/eos_bulk/MACE-MPA_processed.parquet b/benchmarks/eos_bulk/MACE-MPA_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..9ba5d9f8195023b64e40ac524adb6806ae4e32f0
--- /dev/null
+++ b/benchmarks/eos_bulk/MACE-MPA_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 394748
diff --git a/benchmarks/eos_bulk/MatterSim.parquet b/benchmarks/eos_bulk/MatterSim.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..1ac19b614e60ddafbd3a5e0ed2599ac3e7daf401
--- /dev/null
+++ b/benchmarks/eos_bulk/MatterSim.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6717650b97782de6f90e4473075410fe4540279eb39338d2234d3c9399079b3
+size 389586
diff --git a/benchmarks/eos_bulk/MatterSim_processed.parquet b/benchmarks/eos_bulk/MatterSim_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..5c7c372a85d6f4bef844bd6dbe68bf4692ee9344
--- /dev/null
+++ b/benchmarks/eos_bulk/MatterSim_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 290191
diff --git a/benchmarks/eos_bulk/ORBv2.parquet b/benchmarks/eos_bulk/ORBv2.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..2cd571ac07630e9883eb0c9e0d854ed646a18d9b
--- /dev/null
+++ b/benchmarks/eos_bulk/ORBv2.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae13c9af1ae7fafe2a42ed4c47e2ba0f036abfa64a87ca517b92d89c62fcbfd9
+size 427105
diff --git a/benchmarks/eos_bulk/ORBv2_processed.parquet b/benchmarks/eos_bulk/ORBv2_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..82bb16f1d7a7ea63f852f9d6f1657e75abc9784f
--- /dev/null
+++ b/benchmarks/eos_bulk/ORBv2_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 402554
diff --git a/benchmarks/eos_bulk/SevenNet.parquet b/benchmarks/eos_bulk/SevenNet.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..5a3bb7cc25bd7752f35f3fbad0154d411b270b97
--- /dev/null
+++ b/benchmarks/eos_bulk/SevenNet.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 428341
diff --git a/benchmarks/eos_bulk/SevenNet_processed.parquet b/benchmarks/eos_bulk/SevenNet_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..0d4a9920f62dd4340134b6a0ab95e77c03b6e181
--- /dev/null
+++ b/benchmarks/eos_bulk/SevenNet_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 398295
diff --git a/benchmarks/eos_bulk/analyze.py b/benchmarks/eos_bulk/analyze.py
new file mode 100644
index 0000000000000000000000000000000000000000..accaee8ca28547e6d06cb8f0c6ae830d93eaa614
--- /dev/null
+++ b/benchmarks/eos_bulk/analyze.py
@@ -0,0 +1,223 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from ase.db import connect
+from scipy import stats
+
+from mlip_arena.models import REGISTRY, MLIPEnum
+
+DATA_DIR = Path(__file__).parent.absolute()
+
+
+def load_wbm_structures():
+ """
+ Load the WBM structures from a ASE DB file.
+ """
+ with connect(DATA_DIR.parent / "wbm_structures.db") as db:
+ for row in db.select():
+ yield row.toatoms(add_additional_information=True)
+
+def gather_results():
+ for model in MLIPEnum:
+ if "eos_bulk" not in REGISTRY[model.name].get("gpu-tasks", []):
+ continue
+
+ if (DATA_DIR / f"{model.name}.parquet").exists():
+ continue
+
+ all_data = []
+
+ for atoms in load_wbm_structures():
+ fpath = Path(model.name) / f"{atoms.info['key_value_pairs']['wbm_id']}.pkl"
+ if not fpath.exists():
+ continue
+
+ all_data.append(pd.read_pickle(fpath))
+
+ df = pd.concat(all_data, ignore_index=True)
+ df.to_parquet(DATA_DIR / f"{model.name}.parquet")
+
+
+def summarize():
+ summary_table = pd.DataFrame(
+ columns=[
+ "model",
+ "energy-diff-flip-times",
+ "tortuosity",
+ "spearman-compression-energy",
+ "spearman-compression-derivative",
+ "spearman-tension-energy",
+ "missing",
+ ]
+ )
+
+
+ for model in MLIPEnum:
+ fpath = DATA_DIR / f"{model.name}.parquet"
+ if not fpath.exists():
+ continue
+ df_raw_results = pd.read_parquet(fpath)
+
+ df_analyzed = pd.DataFrame(
+ columns=[
+ "model",
+ "structure",
+ "formula",
+ "volume-ratio",
+ "energy-delta-per-atom",
+ "energy-diff-flip-times",
+ "energy-delta-per-volume-b0",
+ "tortuosity",
+ "spearman-compression-energy",
+ "spearman-compression-derivative",
+ "spearman-tension-energy",
+ "missing",
+ ]
+ )
+
+ for wbm_struct in load_wbm_structures():
+ structure_id = wbm_struct.info["key_value_pairs"]["wbm_id"]
+
+ try:
+ results = df_raw_results.loc[df_raw_results["id"] == structure_id]
+ b0 = results["b0"].values[0]
+ # vol0 = results["v0"].values[0]
+ results = results["eos"].values[0]
+ es = np.array(results["energies"])
+ vols = np.array(results["volumes"])
+
+ indices = np.argsort(vols)
+ vols = vols[indices]
+ es = es[indices]
+
+ imine = len(es) // 2
+ # min_center_val = np.min(es[imid - 1 : imid + 2])
+ # imine = np.where(es == min_center_val)[0][0]
+ emin = es[imine]
+ vol0 = vols[imine]
+
+ interpolated_volumes = [
+ (vols[i] + vols[i + 1]) / 2 for i in range(len(vols) - 1)
+ ]
+ ediff = np.diff(es)
+ ediff_sign = np.sign(ediff)
+ mask = ediff_sign != 0
+ ediff = ediff[mask]
+ ediff_sign = ediff_sign[mask]
+ ediff_flip = np.diff(ediff_sign) != 0
+
+ etv = np.sum(np.abs(np.diff(es)))
+
+ data = {
+ "model": model.name,
+ "structure": structure_id,
+ "formula": wbm_struct.get_chemical_formula(),
+ "missing": False,
+ "volume-ratio": vols / vol0,
+ "energy-delta-per-atom": (es - emin) / len(wbm_struct),
+ "energy-diff-flip-times": np.sum(ediff_flip).astype(int),
+ "energy-delta-per-volume-b0": (es - emin) / (b0*vol0),
+ "tortuosity": etv / (abs(es[0] - emin) + abs(es[-1] - emin)),
+ "spearman-compression-energy": stats.spearmanr(
+ vols[:imine], es[:imine]
+ ).statistic,
+ "spearman-compression-derivative": stats.spearmanr(
+ interpolated_volumes[:imine], ediff[:imine]
+ ).statistic,
+ "spearman-tension-energy": stats.spearmanr(
+ vols[imine:], es[imine:]
+ ).statistic,
+ }
+
+ except Exception as e:
+ print(e)
+ data = {
+ "model": model.name,
+ "structure": structure_id,
+ "formula": wbm_struct.get_chemical_formula(),
+ "missing": True,
+ "volume-ratio": None,
+ "energy-delta-per-atom": None,
+ "energy-delta-per-volume-b0": None,
+ "energy-diff-flip-times": None,
+ "tortuosity": None,
+ "spearman-compression-energy": None,
+ "spearman-compression-derivative": None,
+ "spearman-tension-energy": None,
+ }
+
+ df_analyzed = pd.concat([df_analyzed, pd.DataFrame([data])], ignore_index=True)
+
+ df_analyzed.to_parquet(DATA_DIR / f"{model.name}_processed.parquet")
+ # json_fpath = DATA_DIR / f"EV_scan_analyzed_{model.name}.json"
+
+ # df_analyzed.to_json(json_fpath, orient="records")
+
+ valid_results = df_analyzed[df_analyzed["missing"] == False]
+
+ analysis_summary = {
+ "model": model.name,
+ "energy-diff-flip-times": valid_results["energy-diff-flip-times"].mean(),
+ "energy-diff-flip-times-std": valid_results["energy-diff-flip-times"].std(),
+ "tortuosity": valid_results["tortuosity"].mean(),
+ "tortuosity-std": valid_results["tortuosity"].std(),
+ "spearman-compression-energy": valid_results[
+ "spearman-compression-energy"
+ ].mean(),
+ "spearman-compression-energy-std": valid_results["spearman-compression-energy"].std(),
+ "spearman-compression-derivative": valid_results[
+ "spearman-compression-derivative"
+ ].mean(),
+ "spearman-compression-derivative-std": valid_results[
+ "spearman-compression-derivative"
+ ].std(),
+ "spearman-tension-energy": valid_results["spearman-tension-energy"].mean(),
+ "spearman-tension-energy-std": valid_results["spearman-tension-energy"].std(),
+ "missing": len(df_analyzed[df_analyzed["missing"] == True]),
+ }
+ summary_table = pd.concat(
+ [summary_table, pd.DataFrame([analysis_summary])], ignore_index=True
+ )
+
+
+ flip_rank = (
+ (summary_table["energy-diff-flip-times"] - 1)
+ .abs()
+ .rank(ascending=True, method="min")
+ )
+ tortuosity_rank = summary_table["tortuosity"].rank(ascending=True, method="min")
+ spearman_compression_energy_rank = summary_table["spearman-compression-energy"].rank(
+ method="min"
+ )
+ spearman_compression_derivative_rank = summary_table[
+ "spearman-compression-derivative"
+ ].rank(ascending=False, method="min")
+ spearman_tension_energy_rank = summary_table["spearman-tension-energy"].rank(
+ ascending=False, method="min"
+ )
+ missing_rank = summary_table["missing"].rank(ascending=True, method="min")
+
+ rank_aggr = (
+ flip_rank
+ + tortuosity_rank
+ + spearman_compression_energy_rank
+ + spearman_compression_derivative_rank
+ + spearman_tension_energy_rank
+ + missing_rank
+ )
+ rank = rank_aggr.rank(method="min")
+
+ summary_table.insert(1, "rank", rank.astype(int))
+ summary_table.insert(2, "rank-aggregation", rank_aggr.astype(int))
+ summary_table = summary_table.sort_values(by="rank", ascending=True)
+ summary_table = summary_table.reset_index(drop=True)
+
+ summary_table.to_csv(DATA_DIR / "summary.csv", index=False)
+ summary_table.to_latex(DATA_DIR / "summary.tex", index=False, float_format="%.3f")
+
+ return summary_table
+
+if __name__ == "__main__":
+ gather_results()
+ summarize()
diff --git a/benchmarks/eos_bulk/eSEN.parquet b/benchmarks/eos_bulk/eSEN.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..0f9ec3b45d27247fb4e0ddfffe9309057413fa1b
--- /dev/null
+++ b/benchmarks/eos_bulk/eSEN.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4503e17151b7376bbd88dc8c4767747e7290e8eae898e050b0a231a5c447e3e6
+size 427652
diff --git a/benchmarks/eos_bulk/eSEN_processed.parquet b/benchmarks/eos_bulk/eSEN_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..96b954d9ea717afb44fcafc6d734f4ddd9d6efa6
--- /dev/null
+++ b/benchmarks/eos_bulk/eSEN_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7f754d8e18f645c1608e86286245c11611d5af34f3bd0bbc4a5b63b851a0dee
+size 393790
diff --git a/benchmarks/eos_bulk/plot.py b/benchmarks/eos_bulk/plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..d88e3fc14908b47b31cad61ec86be7b11d0ecb91
--- /dev/null
+++ b/benchmarks/eos_bulk/plot.py
@@ -0,0 +1,119 @@
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from ase.db import connect
+
+from mlip_arena.models import REGISTRY as MODELS
+
+DATA_DIR = Path(__file__).parent.absolute()
+
+# Use a qualitative color palette from matplotlib
+palette_name = "tab10" # Better for distinguishing multiple lines
+color_sequence = plt.get_cmap(palette_name).colors
+
+valid_models = [
+ model
+ for model, metadata in MODELS.items()
+ if "eos_bulk" in metadata.get("gpu-tasks", [])
+]
+
+def load_wbm_structures():
+ """
+ Load the WBM structures from a ASE DB file.
+ """
+ with connect(DATA_DIR.parent / "wbm_structures.db") as db:
+ for row in db.select():
+ yield row.toatoms(add_additional_information=True)
+
+# # Collect valid models first
+# valid_models = []
+# for model_name in valid_models:
+# fpath = DATA_DIR / f"{model_name}_processed.parquet"
+# if fpath.exists():
+# df = pd.read_parquet(fpath)
+# if len(df) > 0:
+# valid_models.append(model)
+
+# # Ensure we're showing all 8 models
+# if len(valid_models) < 8:
+# print(f"Warning: Only found {len(valid_models)} valid models instead of 8")
+
+# Set up the grid layout
+n_models = len(valid_models)
+n_cols = 4 # Use 4 columns
+n_rows = (n_models + n_cols - 1) // n_cols # Ceiling division to get required rows
+
+# Create figure with enough space for all subplots
+fig = plt.figure(
+ figsize=(6, 1.25 * n_rows), # Wider for better readability
+ constrained_layout=True, # Better than tight_layout for this case
+)
+
+# Create grid of subplots
+axes = []
+for i in range(n_models):
+ ax = plt.subplot(n_rows, n_cols, i+1)
+ axes.append(ax)
+
+SMALL_SIZE = 6
+MEDIUM_SIZE = 8
+LARGE_SIZE = 10
+
+# Fill in the subplots with data
+for i, model_name in enumerate(valid_models):
+ fpath = DATA_DIR / f"{model_name}_processed.parquet"
+ df = pd.read_parquet(fpath)
+
+ ax = axes[i]
+ valid_structures = []
+
+ for j, (_, row) in enumerate(df.iterrows()):
+ structure_id = row["structure"]
+ formula = row.get("formula", "")
+ if isinstance(row["volume-ratio"], (list, np.ndarray)) and isinstance(
+ row["energy-delta-per-volume-b0"], (list, np.ndarray)
+ ):
+ vol_strain = row["volume-ratio"]
+ energy_delta = row["energy-delta-per-volume-b0"]
+ color = color_sequence[j % len(color_sequence)]
+ ax.plot(
+ vol_strain,
+ energy_delta,
+ color=color,
+ linewidth=1,
+ alpha=0.9,
+ )
+ valid_structures.append(structure_id)
+
+ # Set subplot title
+ ax.set_title(f"{model_name} ({len(valid_structures)})", fontsize=MEDIUM_SIZE)
+
+ # Only add y-label to leftmost plots (those with index divisible by n_cols)
+ if i % n_cols == 0:
+ ax.set_ylabel("$\\frac{\\Delta E}{B V_0}$", fontsize=MEDIUM_SIZE)
+ else:
+ ax.set_ylabel("")
+
+ # Only add x-label to bottom row plots
+ # Check if this plot is in the bottom row
+ is_bottom_row = (i // n_cols) == (n_rows - 1) or (i >= n_models - n_cols)
+ if is_bottom_row:
+ ax.set_xlabel("$V/V_0$", fontsize=MEDIUM_SIZE)
+ else:
+ ax.set_xlabel("")
+
+ ax.set_ylim(-0.02, 0.1) # Consistent y-limits
+ ax.axvline(x=1, linestyle="--", color="gray", alpha=0.7)
+ ax.tick_params(axis="both", which="major", labelsize=MEDIUM_SIZE)
+
+# Make sure all subplots share the x and y limits
+for ax in axes:
+ ax.set_xlim(0.8, 1.2) # Adjust these as needed
+ ax.set_ylim(-0.02, 0.1)
+
+# Save the figure with all plots
+plt.savefig(DATA_DIR / "eos-bulk-grid.png", dpi=300, bbox_inches="tight")
+plt.savefig(DATA_DIR / "eos-bulk-grid.pdf", bbox_inches="tight")
+# plt.show()
\ No newline at end of file
diff --git a/benchmarks/eos_bulk/preprocessing.py b/benchmarks/eos_bulk/preprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..08789185c32c110dd37ce3576bf8779073615069
--- /dev/null
+++ b/benchmarks/eos_bulk/preprocessing.py
@@ -0,0 +1,12 @@
+import json
+
+from ase.db import connect
+from pymatgen.core import Structure
+
+with open("wbm_structures.json") as f:
+ structs = json.load(f)
+
+with connect("wbm_structures.db") as db:
+ for id, s in structs.items():
+ atoms = Structure.from_dict(s).to_ase_atoms(msonable=False)
+ db.write(atoms, wbm_id=id)
diff --git a/benchmarks/eos_bulk/run.py b/benchmarks/eos_bulk/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb1ae5f9076ea70c4901e98979efdd02e1614233
--- /dev/null
+++ b/benchmarks/eos_bulk/run.py
@@ -0,0 +1,170 @@
+# import functools
+from pathlib import Path
+
+import pandas as pd
+from ase import Atoms
+from ase.db import connect
+from dask.distributed import Client
+from dask_jobqueue import SLURMCluster
+from prefect import flow, task
+from prefect.cache_policies import INPUTS, TASK_SOURCE
+from prefect.runtime import task_run
+from prefect_dask import DaskTaskRunner
+
+from mlip_arena.models import REGISTRY, MLIPEnum
+from mlip_arena.tasks.utils import get_calculator
+
+
+@task
+def load_wbm_structures():
+ """
+ Load the WBM structures from an ASE database file.
+
+ Reads structures from 'wbm_structures.db' and yields them as ASE Atoms objects
+ with additional metadata preserved from the database.
+
+ Yields:
+ ase.Atoms: Individual atomic structures from the WBM database with preserved
+ metadata in the .info dictionary.
+ """
+ with connect("../wbm_structures.db") as db:
+ for row in db.select():
+ yield row.toatoms(add_additional_information=True)
+
+
+# def save_result(
+# tsk: Task,
+# run: TaskRun,
+# state: State,
+# model_name: str,
+# id: str,
+# ):
+# result = run.state.result()
+
+# assert isinstance(result, dict)
+
+# result["method"] = model_name
+# result["id"] = id
+# result.pop("atoms", None)
+
+# fpath = Path(f"{model_name}")
+# fpath.mkdir(exist_ok=True)
+
+# fpath = fpath / f"{result['id']}.pkl"
+
+# df = pd.DataFrame([result])
+# df.to_pickle(fpath)
+
+
+@task(
+ name="EOS bulk - WBM",
+ task_run_name=lambda: f"{task_run.task_name}: {task_run.parameters['atoms'].get_chemical_formula()} - {task_run.parameters['model'].name}",
+ cache_policy=TASK_SOURCE + INPUTS,
+)
+def eos_bulk(atoms: Atoms, model: MLIPEnum):
+
+ from mlip_arena.tasks.eos import run as EOS
+ from mlip_arena.tasks.optimize import run as OPT
+
+ calculator = get_calculator(
+ model
+ ) # avoid sending entire model over prefect and select freer GPU
+
+ result = OPT.with_options(
+ refresh_cache=True,
+ )(
+ atoms,
+ calculator,
+ optimizer="FIRE",
+ criterion=dict(
+ fmax=0.1,
+ ),
+ )
+ result = EOS.with_options(
+ refresh_cache=True,
+ # on_completion=[functools.partial(
+ # save_result,
+ # model_name=model.name,
+ # id=atoms.info["key_value_pairs"]["wbm_id"],
+ # )],
+ )(
+ atoms=result["atoms"],
+ calculator=calculator,
+ optimizer="FIRE",
+ npoints=21,
+ max_abs_strain=0.2,
+ concurrent=False
+ )
+
+ result["method"] = model.name
+ result["id"] = atoms.info["key_value_pairs"]["wbm_id"]
+ result.pop("atoms", None)
+
+ fpath = Path(f"{model.name}")
+ fpath.mkdir(exist_ok=True)
+
+ fpath = fpath / f"{result['id']}.pkl"
+
+ df = pd.DataFrame([result])
+ df.to_pickle(fpath)
+
+ return df
+
+
+@flow
+def submit_tasks():
+ futures = []
+ for atoms in load_wbm_structures():
+ model = MLIPEnum["eSEN"]
+ # for model in MLIPEnum:
+ if "eos_bulk" not in REGISTRY[model.name].get("gpu-tasks", []):
+ continue
+ try:
+ result = eos_bulk.with_options(
+ refresh_cache=True
+ ).submit(atoms, model)
+ futures.append(result)
+ except Exception:
+ # print(f"Failed to submit task for {model.name}: {e}")
+ continue
+ return [f.result(raise_on_failure=False) for f in futures]
+
+
+if __name__ == "__main__":
+ nodes_per_alloc = 1
+ gpus_per_alloc = 1
+ ntasks = 1
+
+ cluster_kwargs = dict(
+ cores=1,
+ memory="64 GB",
+ shebang="#!/bin/bash",
+ account="m3828",
+ walltime="00:30:00",
+ job_mem="0",
+ job_script_prologue=[
+ "source ~/.bashrc",
+ "module load python",
+ "module load cudatoolkit/12.4",
+ "source activate /pscratch/sd/c/cyrusyc/.conda/dev",
+ ],
+ job_directives_skip=["-n", "--cpus-per-task", "-J"],
+ job_extra_directives=[
+ "-J eos_bulk",
+ "-q regular",
+ f"-N {nodes_per_alloc}",
+ "-C gpu",
+ f"-G {gpus_per_alloc}",
+ # "--exclusive",
+ ],
+ )
+
+ cluster = SLURMCluster(**cluster_kwargs)
+ print(cluster.job_script())
+ cluster.adapt(minimum_jobs=50, maximum_jobs=50)
+ client = Client(cluster)
+
+ submit_tasks.with_options(
+ task_runner=DaskTaskRunner(address=client.scheduler.address),
+ log_prints=True,
+ )()
diff --git a/benchmarks/eos_bulk/summary.csv b/benchmarks/eos_bulk/summary.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ff414cbbb09846d4b25c43ca839d719527fcabe8
--- /dev/null
+++ b/benchmarks/eos_bulk/summary.csv
@@ -0,0 +1,9 @@
+model,rank,rank-aggregation,energy-diff-flip-times,tortuosity,spearman-compression-energy,spearman-compression-derivative,spearman-tension-energy,missing,energy-diff-flip-times-std,tortuosity-std,spearman-compression-energy-std,spearman-compression-derivative-std,spearman-tension-energy-std
+MACE-MPA,1,7,1.0370741482965933,1.005455197941088,-0.9993684338373716,0.9963320580555048,0.993186372745491,2,0.28260902337559207,0.05365793240575371,0.01247051827833709,0.03852356148675327,0.07744103059608153
+eSEN,2,15,1.042211055276382,1.0082267858369258,-0.9993299832495811,0.9968570123343992,0.9920968478757424,5,0.31435657463218236,0.09009343693321628,0.012254373724862471,0.03683033142639347,0.07346152527758068
+MACE-MP(M),3,20,1.042211055276382,1.008986842539345,-0.999329983249581,0.9941160347190496,0.9915857612939804,5,0.3448779209525529,0.1291612188691875,0.011378760248143813,0.059100297945675236,0.0879944289437058
+MatterSim,4,22,1.045135406218656,1.0060900449752808,-0.99734962463147,0.9927904926901917,0.9880977115916667,3,0.376211439097473,0.055231139063835144,0.03888149868978045,0.07797796889169765,0.11523169167576831
+CHGNet,5,27,1.1053159478435306,1.014753469076796,-0.9964985866690981,0.9929971733381963,0.9866417434120545,3,0.5395426308257233,0.12255069852201543,0.051058628207987275,0.05245296840977506,0.11650035215147045
+SevenNet,6,32,1.1093279839518555,1.0186969977862483,-0.9981277164827815,0.9889121911188109,0.9859580417030127,3,0.5552625647497746,0.2749956370938698,0.025627387238441334,0.07657439969351316,0.11715556163493943
+M3GNet,7,38,1.1748743718592964,1.0175007963267957,-0.9963209989340641,0.9897426526572255,0.9801690217498693,5,0.6755070078112404,0.1488462321310986,0.05166730302469224,0.06468954475570607,0.1332400556108672
+ORBv2,8,48,1.3162134944612287,1.0374718753890275,-0.9918459519667977,0.9701425127407,0.9637462235649547,7,0.8699212994733451,0.2149179445701606,0.08208261674781654,0.1315974423719716,0.19758814985102582
diff --git a/benchmarks/eos_bulk/summary.tex b/benchmarks/eos_bulk/summary.tex
new file mode 100644
index 0000000000000000000000000000000000000000..708918a4b061a9590625eeff08bc9283d17278b7
--- /dev/null
+++ b/benchmarks/eos_bulk/summary.tex
@@ -0,0 +1,14 @@
+\begin{tabular}{lrrrrrrrlrrrrr}
+\toprule
+model & rank & rank-aggregation & energy-diff-flip-times & tortuosity & spearman-compression-energy & spearman-compression-derivative & spearman-tension-energy & missing & energy-diff-flip-times-std & tortuosity-std & spearman-compression-energy-std & spearman-compression-derivative-std & spearman-tension-energy-std \\
+\midrule
+MACE-MPA & 1 & 7 & 1.037 & 1.005 & -0.999 & 0.996 & 0.993 & 2 & 0.283 & 0.054 & 0.012 & 0.039 & 0.077 \\
+eSEN & 2 & 15 & 1.042 & 1.008 & -0.999 & 0.997 & 0.992 & 5 & 0.314 & 0.090 & 0.012 & 0.037 & 0.073 \\
+MACE-MP(M) & 3 & 20 & 1.042 & 1.009 & -0.999 & 0.994 & 0.992 & 5 & 0.345 & 0.129 & 0.011 & 0.059 & 0.088 \\
+MatterSim & 4 & 22 & 1.045 & 1.006 & -0.997 & 0.993 & 0.988 & 3 & 0.376 & 0.055 & 0.039 & 0.078 & 0.115 \\
+CHGNet & 5 & 27 & 1.105 & 1.015 & -0.996 & 0.993 & 0.987 & 3 & 0.540 & 0.123 & 0.051 & 0.052 & 0.117 \\
+SevenNet & 6 & 32 & 1.109 & 1.019 & -0.998 & 0.989 & 0.986 & 3 & 0.555 & 0.275 & 0.026 & 0.077 & 0.117 \\
+M3GNet & 7 & 38 & 1.175 & 1.018 & -0.996 & 0.990 & 0.980 & 5 & 0.676 & 0.149 & 0.052 & 0.065 & 0.133 \\
+ORBv2 & 8 & 48 & 1.316 & 1.037 & -0.992 & 0.970 & 0.964 & 7 & 0.870 & 0.215 & 0.082 & 0.132 & 0.198 \\
+\bottomrule
+\end{tabular}
diff --git a/benchmarks/force_equivariance/run.py b/benchmarks/force_equivariance/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..247aa6dce766d0adfa9101f732705c813882aeaa
--- /dev/null
+++ b/benchmarks/force_equivariance/run.py
@@ -0,0 +1,307 @@
+"""
+Define equivariance testing task.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from pathlib import Path
+
+import numpy as np
+from ase import Atoms
+from prefect import task
+from scipy.spatial.transform import Rotation as R
+from tqdm import tqdm
+
+
+def generate_random_unit_vector():
+ """Generate a random unit vector."""
+ vec = np.random.normal(0, 1, 3)
+ return vec / np.linalg.norm(vec)
+
+
+def rotate_molecule_arbitrary(
+ atoms: Atoms, angle: float, axis: np.ndarray
+) -> tuple[Atoms, np.ndarray]:
+ """Rotate molecule around arbitrary axis."""
+ rotated_atoms = atoms.copy()
+ positions = rotated_atoms.get_positions()
+ rot = R.from_rotvec(np.radians(angle) * axis)
+ rotation_mat = rot.as_matrix()
+ rotated_positions = rot.apply(positions)
+ rotated_atoms.set_positions(rotated_positions)
+ cell = atoms.get_cell()
+ rotated_cell = rot.apply(cell)
+ rotated_atoms.set_cell(rotated_cell)
+ return rotated_atoms, rotation_mat
+
+
+def compare_forces(
+ original_forces: np.ndarray,
+ rotated_forces: np.ndarray,
+ rotation_mat: np.ndarray,
+ zero_threshold: float = 1e-10,
+) -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:
+ """
+ Compare forces before and after rotation, with handling of 0 force case.
+
+ Args:
+ original_forces: Forces before rotation (N x 3 array)
+ rotated_forces: Forces after rotation (N x 3 array)
+ rotation_mat: 3 x 3 rotation matrix
+ zero_threshold: Threshold below which forces are considered zero
+
+ Returns:
+ tuple containing:
+ - mae: Mean absolute error between forces
+ - cosine_similarity: Cosine similarity between force vectors
+ """
+ rotated_original_forces = np.dot(original_forces, rotation_mat.T)
+ force_diff = rotated_original_forces - rotated_forces
+ mae = np.mean(np.abs(force_diff))
+
+ original_magnitudes = np.linalg.norm(rotated_original_forces, axis=1)
+ rotated_magnitudes = np.linalg.norm(rotated_forces, axis=1)
+
+ zero_original = original_magnitudes < zero_threshold
+ zero_rotated = rotated_magnitudes < zero_threshold
+ both_zero = zero_original & zero_rotated
+ either_zero = zero_original | zero_rotated
+ one_zero = either_zero & ~both_zero
+
+ cosine_similarity = np.zeros(len(original_forces))
+
+ valid_forces = ~either_zero
+ if np.any(valid_forces):
+ norms_product = np.linalg.norm(
+ rotated_original_forces[valid_forces], axis=1
+ ) * np.linalg.norm(rotated_forces[valid_forces], axis=1)
+ dot_products = np.sum(
+ rotated_original_forces[valid_forces] * rotated_forces[valid_forces], axis=1
+ )
+ cosine_similarity[valid_forces] = dot_products / norms_product
+
+ # If both forces are 0, cosine similarity should be 1. If one is 0, we take the conservative -1.
+ cosine_similarity[both_zero] = 1.0
+ cosine_similarity[one_zero] = -1.0
+
+ return mae, cosine_similarity
+
+
+def save_molecule_results(
+ aggregate_results: dict, idx_list: np.ndarray, save_path: str | Path
+) -> None:
+ """
+ Save all molecule results from equivariance testing to .npy files.
+ Save the index list of the atoms for further analysis.
+
+ Args:
+ aggregate_results: Dictionary containing the aggregated results from run()
+ idx_list: List of the indices of the atoms in the original dataset
+ save_path: Path to save the .npy files
+ """
+ save_path = Path(save_path)
+ save_path.parent.mkdir(parents=True, exist_ok=True)
+
+ all_molecule_results = aggregate_results["molecule_results"]
+ rotation_angles = list(all_molecule_results[0]["results_by_angle"].keys())
+
+ num_molecules = len(all_molecule_results)
+ num_angles = len(rotation_angles)
+ num_random_axes = len(
+ all_molecule_results[0]["results_by_angle"][rotation_angles[0]]["maes"]
+ )
+ num_atoms = len(
+ all_molecule_results[0]["results_by_angle"][rotation_angles[0]][
+ "cosine_similarities"
+ ][0]
+ )
+
+ maes = np.zeros((num_molecules, num_angles, num_random_axes))
+ cosine_similarities = np.zeros((num_molecules, num_angles, num_random_axes))
+
+ for mol_idx, molecule in enumerate(all_molecule_results):
+ for angle_idx, angle in enumerate(rotation_angles):
+ angle_results = molecule["results_by_angle"][angle]
+ maes[mol_idx, angle_idx, :] = angle_results["maes"]
+ cosine_similarities[mol_idx, angle_idx, :] = np.mean(
+ angle_results["cosine_similarities"], axis=-1
+ )
+
+ np.save(save_path.with_name(f"{save_path.stem}_maes.npy"), maes)
+ np.save(
+ save_path.with_name(f"{save_path.stem}_cosine_similarities.npy"),
+ cosine_similarities,
+ )
+ np.save(save_path.with_name(f"{save_path.stem}_idx_list.npy"), idx_list)
+
+
+@task(
+ name="Equivariance testing",
+ task_run_name=_generate_task_run_name,
+ cache_policy=TASK_SOURCE + INPUTS,
+)
+def run(
+ atoms_list: Sequence[Atoms],
+ idx_list: np.ndarray,
+ calculator: BaseCalculator,
+ save_path: str | Path | None = None,
+ rotation_angles: list[float] | np.ndarray = None,
+ num_random_axes: int = 100,
+ threshold: float = 1e-3,
+ seed: int | None = None,
+) -> dict:
+ """
+ Test equivariance of force predictions under rotations for multiple structures.
+
+ Args:
+ atoms_list: List of input atomic structures
+ idx_list: List of the indices of the atoms in the original dataset
+ calculator: Calculator to use
+ num_rotations: Number of random rotations to test
+ rotation_angle: Angle of rotation in degrees
+ threshold: Threshold for considering forces equivariant
+ seed: Random seed
+
+ Returns:
+ Dictionary containing test results
+ """
+ if seed is not None:
+ np.random.seed(seed)
+
+ if rotation_angles is None:
+ rotation_angles = np.arange(30, 361, 30)
+ rotation_angles = np.array(rotation_angles)
+
+ all_results = []
+
+ cross_molecule_cosine_sims = {angle: [] for angle in rotation_angles}
+ cross_molecule_mae = {angle: [] for angle in rotation_angles}
+
+ rotation_axes = [generate_random_unit_vector() for _ in range(num_random_axes)]
+
+ total_tests = len(atoms_list) * len(rotation_angles) * num_random_axes
+ pbar = tqdm(total=total_tests, desc="Testing rotations")
+
+ for atom_idx, atoms in enumerate(atoms_list):
+ atoms = atoms.copy()
+ atoms.calc = calculator
+ original_forces = atoms.get_forces()
+
+ results_by_angle = {
+ angle: {
+ "mae": [],
+ "cosine_similarities": [],
+ "passed_tests": 0,
+ "passed_mae": 0,
+ "passed_cosine_similarity": 0,
+ }
+ for angle in rotation_angles
+ }
+ # Test each angle with multiple random axes
+ for angle in rotation_angles:
+ for axis in rotation_axes:
+ rotated_atoms, rotation_mat = rotate_molecule_arbitrary(
+ atoms, angle, axis
+ )
+ rotated_atoms.calc = calculator
+ rotated_forces = rotated_atoms.get_forces()
+ mae, cosine_similarity = compare_forces(
+ original_forces, rotated_forces, rotation_mat
+ )
+ results_by_angle[angle]["mae"].append(mae)
+ results_by_angle[angle]["cosine_similarities"].append(cosine_similarity)
+
+ cross_molecule_cosine_sims[angle].append(
+ float(np.mean(cosine_similarity))
+ )
+ cross_molecule_mae[angle].append(float(np.mean(mae)))
+
+ mae_check = mae < threshold
+ cosine_check = all(cosine_similarity > (1 - threshold))
+ results_by_angle[angle]["passed_tests"] += int(
+ mae_check and cosine_check
+ )
+ results_by_angle[angle]["passed_mae"] += int(mae_check)
+ results_by_angle[angle]["passed_cosine_similarity"] += int(cosine_check)
+
+ pbar.update(1)
+ # Compute summary statistics
+ for angle in rotation_angles:
+ results = results_by_angle[angle]
+ results["mean_cosine_similarity"] = float(
+ np.mean(results["cosine_similarities"])
+ )
+ results["avg_mae"] = float(np.mean(results["mae"]))
+ results["equivariant_ratio"] = results["passed_tests"] / num_random_axes
+ results["mae_passed_ratio"] = results["passed_mae"] / num_random_axes
+ results["cosine_passed_ratio"] = (
+ results["passed_cosine_similarity"] / num_random_axes
+ )
+ results["passed"] = results["passed_tests"] == num_random_axes
+ results["passed_mae"] = results["passed_mae"] == num_random_axes
+ results["passed_cosine_similarity"] = (
+ results["passed_cosine_similarity"] == num_random_axes
+ )
+ results["maes"] = [float(x) for x in results["mae"]]
+ results["cosine_similarities"] = [
+ [float(y) for y in x] for x in results["cosine_similarities"]
+ ]
+
+ molecule_results = {
+ "mol_idx": idx_list[atom_idx],
+ "results_by_angle": results_by_angle,
+ "all_passed": all(
+ results_by_angle[angle]["passed"] for angle in rotation_angles
+ ),
+ "avg_cosine_similarity_by_molecule": float(
+ np.mean(
+ [
+ results_by_angle[angle]["mean_cosine_similarity"]
+ for angle in rotation_angles
+ ]
+ )
+ ),
+ "avg_mae_by_molecule": float(
+ np.mean(
+ [results_by_angle[angle]["avg_mae"] for angle in rotation_angles]
+ )
+ ),
+ "overall_equivariant_ratio": float(
+ np.mean(
+ [
+ results_by_angle[angle]["equivariant_ratio"]
+ for angle in rotation_angles
+ ]
+ )
+ ),
+ }
+
+ all_results.append(molecule_results)
+
+ pbar.close()
+
+ aggregate_results = {
+ "num_molecules": len(atoms_list),
+ "all_molecules_passed": all(result["all_passed"] for result in all_results),
+ "average_equivariant_ratio": float(
+ np.mean([result["overall_equivariant_ratio"] for result in all_results])
+ ),
+ "average_cosine_similarity_by_angle": {
+ angle: float(np.mean(sims))
+ for angle, sims in cross_molecule_cosine_sims.items()
+ },
+ "average_mae_by_angle": {
+ angle: float(np.mean(diffs)) for angle, diffs in cross_molecule_mae.items()
+ },
+ "molecule_results": all_results,
+ }
+
+ if save_path:
+ save_molecule_results(aggregate_results, idx_list, save_path)
+ np.save(
+ str(save_path.with_name(f"{save_path.stem}_molecule_results.npy")),
+ all_results,
+ )
+
+ return aggregate_results
diff --git a/benchmarks/mof/classification/M3GNet.pkl b/benchmarks/mof/classification/M3GNet.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..e487b4ad1ecae8574a76621fe9a878cc545ff0af
--- /dev/null
+++ b/benchmarks/mof/classification/M3GNet.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f3e954dba20470846a14796b21dceeac03c02db8613527d441f387063697efe
+size 218426
diff --git a/benchmarks/mof/classification/MACE-MP(M).pkl b/benchmarks/mof/classification/MACE-MP(M).pkl
new file mode 100644
index 0000000000000000000000000000000000000000..550d35c31c694ae17fa185a3acbe19d9d041f3ec
--- /dev/null
+++ b/benchmarks/mof/classification/MACE-MP(M).pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d0967389772b92764ecc9a521d83fefff9f99ac365bbd8ba643bec32313ce57
+size 197302
diff --git a/benchmarks/mof/classification/MACE-MPA.pkl b/benchmarks/mof/classification/MACE-MPA.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..a848a5324eb3d9d3623d0bbd46d409f0deb2cefd
--- /dev/null
+++ b/benchmarks/mof/classification/MACE-MPA.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d8ca57b95e2089ea86e28b3866bc3118e0893832a0be94ca9399002440ea4e3
+size 299928
diff --git a/benchmarks/mof/classification/MatterSim.pkl b/benchmarks/mof/classification/MatterSim.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..ecb06b552cfa6e3b467561176b520d25c8fa1867
--- /dev/null
+++ b/benchmarks/mof/classification/MatterSim.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e7466643aedc9c6ea0b2a34fa9227fa99eeb28c67818de077b7b5edd8d49bf47
+size 298511
diff --git a/benchmarks/mof/classification/ORBv2.pkl b/benchmarks/mof/classification/ORBv2.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..8c0b927957a1763e33039d1c59a393f923552117
--- /dev/null
+++ b/benchmarks/mof/classification/ORBv2.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69c635f37e99782d0d0f2eb413f57285df0ed4666e390a2171f4d4b2b2febf36
+size 248466
diff --git a/benchmarks/mof/classification/SevenNet.pkl b/benchmarks/mof/classification/SevenNet.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..d3efe3a636798a9484198768921c78bb33f19610
--- /dev/null
+++ b/benchmarks/mof/classification/SevenNet.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8285152e645f30facb7c53298075fd6b7483149944c98bf52f46bb1c3e62b67
+size 249418
diff --git a/benchmarks/mof/classification/analysis.ipynb b/benchmarks/mof/classification/analysis.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9e67b3ae920832e91f1f3e3140a1e7c94e00172c
--- /dev/null
+++ b/benchmarks/mof/classification/analysis.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "import pandas as pd\n",
+ "import plotly.colors as pcolors\n",
+ "import seaborn as sns\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "from mlip_arena.models import MLIPEnum\n",
+ "\n",
+ "mlip_methods = [\n",
+ " model.name\n",
+ " for model in MLIPEnum\n",
+ "]\n",
+ "\n",
+ "all_attributes = dir(pcolors.qualitative)\n",
+ "color_palettes = {\n",
+ " attr: getattr(pcolors.qualitative, attr)\n",
+ " for attr in all_attributes\n",
+ " if isinstance(getattr(pcolors.qualitative, attr), list)\n",
+ "}\n",
+ "color_palettes.pop(\"__all__\", None)\n",
+ "\n",
+ "palette_names = list(color_palettes.keys())\n",
+ "palette_colors = list(color_palettes.values())\n",
+ "palette_name = \"Plotly\"\n",
+ "color_sequence = color_palettes[palette_name] # type: ignore\n",
+ "\n",
+ "method_color_mapping = {\n",
+ " method: color_sequence[i % len(color_sequence)]\n",
+ " for i, method in enumerate(mlip_methods)\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "from mlip_arena.models import MLIPEnum\n",
+ "\n",
+ "# Color mapping by class\n",
+ "color_mapping = {\n",
+ " \"DAC\": \"#e41a1c\",\n",
+ " \"Flue Gas\": \"#377eb8\",\n",
+ " \"General\": \"#4daf4a\"\n",
+ "}\n",
+ "\n",
+ "# Decision boundary thresholds\n",
+ "thresholds = {\n",
+ " \"General\": (None, 35),\n",
+ " \"Flue Gas\": (35, 50),\n",
+ " \"DAC\": (50, 100)\n",
+ "}\n",
+ "\n",
+ "# Collect data from all models\n",
+ "all_data = []\n",
+ "margins = []\n",
+ "\n",
+ "for model in MLIPEnum:\n",
+ " fpath = Path(f\"{model.name}.pkl\")\n",
+ " if not fpath.exists():\n",
+ " continue\n",
+ "\n",
+ " df = pd.read_pickle(fpath)\n",
+ " df = df.drop_duplicates(subset=[\"model\", \"name\", \"class\"], keep=\"last\")\n",
+ " df_exploded = df.explode([\"henry_coefficient\", \"averaged_interaction_energy\", \"heat_of_adsorption\"])\n",
+ " df_group = df_exploded.groupby([\"model\", \"name\", \"class\"])[[\"henry_coefficient\", \"averaged_interaction_energy\", \"heat_of_adsorption\"]].mean().reset_index()\n",
+ "\n",
+ " df_group[\"model_name\"] = model.name\n",
+ " df_group[\"neg_heat\"] = -df_group[\"heat_of_adsorption\"] # negate for log scale\n",
+ " df_group = df_group[df_group[\"neg_heat\"] > 0] # remove invalid values\n",
+ "\n",
+ " df_group = df_group[df_group[\"name\"] != \"MIL-96-Al\"]\n",
+ "\n",
+ " all_data.append(df_group)\n",
+ "\n",
+ " # Compute misclassification margin\n",
+ " def point_misclassified(row):\n",
+ " val = row[\"neg_heat\"]\n",
+ " lower, upper = thresholds[row[\"class\"]]\n",
+ " return (lower is not None and val < lower) or (upper is not None and val >= upper)\n",
+ "\n",
+ " misclassified = df_group[df_group.apply(point_misclassified, axis=1)]\n",
+ "\n",
+ " def distance_to_boundary(row):\n",
+ " val = row[\"neg_heat\"]\n",
+ " lower, upper = thresholds[row[\"class\"]]\n",
+ " distances = []\n",
+ " if lower is not None:\n",
+ " distances.append(abs(val - lower))\n",
+ " if upper is not None:\n",
+ " distances.append(abs(val - upper))\n",
+ " return min(distances)\n",
+ "\n",
+ " if not misclassified.empty:\n",
+ " num_misclassified = len(misclassified) + (18 - len(df_group))\n",
+ " margin = misclassified.apply(distance_to_boundary, axis=1).mean()\n",
+ " else:\n",
+ " num_misclassified = 0\n",
+ " margin = 0.0\n",
+ "\n",
+ " margins.append((model.name, margin, num_misclassified))\n",
+ "\n",
+ "\n",
+ "# Combine all into one DataFrame\n",
+ "combined_df = pd.concat(all_data, ignore_index=True)\n",
+ "margins_df = pd.DataFrame(margins, columns=[\"model_name\", \"misclassification_margin\", \"num_misclassified\"])\n",
+ "\n",
+ "# --- Plotting ---\n",
+ "\n",
+ "with plt.style.context(\"default\"):\n",
+ "\n",
+ " LARGE_SIZE = 10\n",
+ " MEDIUM_SIZE = 8\n",
+ " SMALL_SIZE = 6\n",
+ "\n",
+ " plt.rcParams.update({\n",
+ " \"font.size\": SMALL_SIZE,\n",
+ " \"axes.titlesize\": MEDIUM_SIZE,\n",
+ " \"axes.labelsize\": MEDIUM_SIZE,\n",
+ " \"xtick.labelsize\": SMALL_SIZE,\n",
+ " \"ytick.labelsize\": SMALL_SIZE,\n",
+ " \"legend.fontsize\": SMALL_SIZE,\n",
+ " \"figure.titlesize\": LARGE_SIZE,\n",
+ " })\n",
+ "\n",
+ " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 4), sharex=False, gridspec_kw={\"height_ratios\": [3, 1.5]})\n",
+ "\n",
+ " # --- Main Stripplot ---\n",
+ " sns.stripplot(\n",
+ " data=combined_df,\n",
+ " x=\"neg_heat\",\n",
+ " y=\"model_name\",\n",
+ " hue=\"class\",\n",
+ " size=2,\n",
+ " palette=color_mapping,\n",
+ " dodge=True,\n",
+ " jitter=0.1,\n",
+ " alpha=1,\n",
+ " ax=ax1,\n",
+ " )\n",
+ "\n",
+ " xmin, xmax = ax1.get_xlim()\n",
+ "\n",
+ " ax1.axvspan(xmin, 35, color=color_mapping[\"General\"], alpha=0.1, label=\"General\")\n",
+ " ax1.axvspan(35, 50, color=color_mapping[\"Flue Gas\"], alpha=0.1, label=\"Flue Gas\")\n",
+ " ax1.axvspan(50, 100, color=color_mapping[\"DAC\"], alpha=0.1, label=\"DAC\")\n",
+ "\n",
+ " ax1.axvline(x=35, linestyle=\"--\", color=\"gray\", label=\"Exp. $\\\\mathregular{CO_2}$ $Q_\\\\text{st}$ = 35 kJ/mol\")\n",
+ " ax1.axvline(x=50, linestyle=\"--\", color=\"gray\", label=\"Exp. $\\\\mathregular{CO_2}$ $Q_\\\\text{st}$ = 50 kJ/mol\")\n",
+ " ax1.axvline(x=100, linestyle=\"--\", color=\"gray\", label=\"Exp. $\\\\mathregular{CO_2}$ $Q_\\\\text{st}$ = 100 kJ/mol\")\n",
+ "\n",
+ " ax1.set_xscale(\"log\")\n",
+ " ax1.set_xlabel(\"Heat of $\\\\mathregular{CO_2}$ Adsorption $Q_\\\\text{st}$ [kJ/mol]\")\n",
+ " ax1.set_ylabel(\"\")\n",
+ " ax1.set_xlim(xmin, xmax)\n",
+ "\n",
+ " yticks = ax1.get_yticks()\n",
+ " yticks = np.array(yticks)\n",
+ " yticks = yticks[np.isfinite(yticks)] # Remove any NaNs\n",
+ "\n",
+ " # Draw horizontal lines between models (skip the last one)\n",
+ " for y in yticks[:-1] + np.diff(yticks) / 2:\n",
+ " ax1.axhline(y=y, color=\"gray\", linestyle=\":\", linewidth=0.7, alpha=0.5, zorder=0)\n",
+ "\n",
+ " handles, labels = ax1.get_legend_handles_labels()\n",
+ " legend_dict = dict(zip(labels, handles, strict=False))\n",
+ "\n",
+ " desired_order = [\n",
+ " \"General\", \"Exp. $\\\\mathregular{CO_2}$ $Q_\\\\text{st}$ = 35 kJ/mol\", \"Flue Gas\",\n",
+ " \"Exp. $\\\\mathregular{CO_2}$ $Q_\\\\text{st}$ = 50 kJ/mol\", \"DAC\", \"Exp. $\\\\mathregular{CO_2}$ $Q_\\\\text{st}$ = 100 kJ/mol\"\n",
+ " ]\n",
+ "\n",
+ " ordered_handles = [legend_dict[label] for label in desired_order if label in legend_dict]\n",
+ "\n",
+ " ax1.legend(\n",
+ " ordered_handles,\n",
+ " desired_order,\n",
+ " loc=\"lower center\",\n",
+ " bbox_to_anchor=(0.5, 1),\n",
+ " ncol=3,\n",
+ " frameon=True\n",
+ " )\n",
+ "\n",
+ "\n",
+ " ax1.spines[\"top\"].set_visible(False)\n",
+ " ax1.spines[\"right\"].set_visible(False)\n",
+ "\n",
+ " # --- Misclassification Margin Barplot ---\n",
+ "\n",
+ " # Sort by error margin\n",
+ " margins_df_sorted = margins_df.sort_values(by=\"misclassification_margin\", ascending=True)\n",
+ "\n",
+ " # Extract color values in order\n",
+ " bar_colors = [method_color_mapping[m] for m in margins_df_sorted[\"model_name\"]]\n",
+ "\n",
+ " sns.scatterplot(\n",
+ " data=margins_df_sorted,\n",
+ " x=\"num_misclassified\",\n",
+ " y=\"misclassification_margin\",\n",
+ " hue=\"model_name\",\n",
+ " palette=bar_colors,\n",
+ " ax=ax2\n",
+ " )\n",
+ "\n",
+ " for _, row in margins_df_sorted.iterrows():\n",
+ " x = row[\"num_misclassified\"]\n",
+ " y = row[\"misclassification_margin\"]\n",
+ " model = row[\"model_name\"]\n",
+ " color = bar_colors[margins_df_sorted[\"model_name\"].tolist().index(model)]\n",
+ "\n",
+ " ax2.text(\n",
+ " x+0.1,\n",
+ " y,\n",
+ " f\"{y:.2f}\",\n",
+ " fontsize=SMALL_SIZE,\n",
+ " ha=\"left\",\n",
+ " va=\"bottom\",\n",
+ " color=color,\n",
+ " alpha=0.9\n",
+ " )\n",
+ "\n",
+ " ax2.set_ylabel(\"Misclass. margin [kJ/mol]\")\n",
+ " ax2.set_xlabel(\"Missing + misclass. count\")\n",
+ " ax2.spines[\"top\"].set_visible(False)\n",
+ " ax2.spines[\"right\"].set_visible(False)\n",
+ " # ax2.set_xticklabels(margins_df_sorted[\"model_name\"], rotation=45)\n",
+ " ax2.set_yscale(\"log\")\n",
+ "\n",
+ " handles, labels = ax2.get_legend_handles_labels()\n",
+ " legend_dict = dict(zip(labels, handles, strict=False))\n",
+ " ax2.legend(\n",
+ " legend_dict.values(),\n",
+ " legend_dict.keys(),\n",
+ " loc=\"upper left\",\n",
+ " bbox_to_anchor=(0, 1),\n",
+ " ncol=3,\n",
+ " frameon=True\n",
+ " )\n",
+ "\n",
+ " plt.tight_layout()\n",
+ " plt.savefig(\"mof-misclassification_margin.pdf\", bbox_inches=\"tight\")\n",
+ " plt.show()\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "mlip-arena",
+ "language": "python",
+ "name": "mlip-arena"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/benchmarks/mof/classification/classification.py b/benchmarks/mof/classification/classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..edf0fae3bd8b2e8c4ae9eb6701cc931cd4a3ff7f
--- /dev/null
+++ b/benchmarks/mof/classification/classification.py
@@ -0,0 +1,152 @@
+import functools
+import itertools
+from pathlib import Path
+
+import pandas as pd
+from ase import Atoms
+from ase.build import molecule
+from dask.distributed import Client
+from dask_jobqueue import SLURMCluster
+from prefect import Task, flow, task
+from prefect.client.schemas.objects import TaskRun
+from prefect.states import State
+from prefect_dask import DaskTaskRunner
+from tqdm.auto import tqdm
+
+from mlip_arena.models import MLIPEnum
+from mlip_arena.tasks.mof.flow import widom_insertion
+from mlip_arena.tasks.utils import get_calculator
+
+
+def load_row_from_df(fpath: str):
+ df = pd.read_pickle(fpath)
+
+ for _, row in df.iterrows():
+ yield row
+
+
+def save_result(
+ tsk: Task,
+ run: TaskRun,
+ state: State,
+ row: pd.DataFrame,
+ model_name: str,
+ gas: Atoms,
+ fpath: str,
+):
+ result = run.state.result()
+
+ assert isinstance(result, dict)
+
+ copied = row.copy()
+ copied["model"] = model_name
+ copied["gas"] = gas
+
+ for k, v in result.items():
+ copied[k] = v
+
+ fpath = Path(f"{model_name}.pkl")
+
+ if fpath.exists():
+ df = pd.read_pickle(fpath)
+ df = pd.concat([df, pd.DataFrame([copied])], ignore_index=True)
+ else:
+ df = pd.DataFrame([copied])
+
+ df.drop_duplicates(subset=["name", "model"], keep="last", inplace=True)
+ df.to_pickle(fpath)
+
+
+# Orchestrate your awesome dask workflow runner
+
+nodes_per_alloc = 1
+gpus_per_alloc = 1
+ntasks = 1
+
+cluster_kwargs = dict(
+ cores=4,
+ memory="64 GB",
+ shebang="#!/bin/bash",
+ account="m3828",
+ walltime="04:00:00",
+ job_mem="0",
+ job_script_prologue=[
+ "source ~/.bashrc",
+ "module load python",
+ "source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena",
+ ],
+ job_directives_skip=["-n", "--cpus-per-task", "-J"],
+ job_extra_directives=[
+ "-J mof",
+ "-q regular",
+ f"-N {nodes_per_alloc}",
+ "-C gpu",
+ f"-G {gpus_per_alloc}",
+ # "--exclusive",
+ ],
+)
+
+cluster = SLURMCluster(**cluster_kwargs)
+print(cluster.job_script())
+cluster.adapt(minimum_jobs=10, maximum_jobs=20)
+client = Client(cluster)
+
+
+@task
+def run_one(model, row, gas):
+ return widom_insertion.with_options(
+ refresh_cache=False,
+ on_completion=[functools.partial(
+ save_result,
+ row=row,
+ model_name=model.name,
+ gas=gas,
+ fpath=f"{model.name}.pkl"
+ )]
+ )(
+ structure=row["structure"],
+ gas=gas,
+ calculator=get_calculator(
+ model,
+ dispersion=True
+ ),
+ criterion=dict(fmax=0.05, steps=50),
+ init_structure_optimize_loops = 10,
+ )
+
+
+@flow
+def run_all():
+ futures = []
+ gas = molecule("CO2")
+
+ for model, row in tqdm(itertools.product(MLIPEnum, load_row_from_df("input.pkl"))):
+
+ if model.name not in ["MACE-MPA", "MatterSim", "SevenNet", "M3GNet", "ORBv2"]:
+ continue
+
+ fpath = Path(f"{model.name}.pkl")
+
+ if fpath.exists():
+ df = pd.read_pickle(fpath)
+ if row['name'] in df['name'].values:
+ continue
+
+ try:
+ print(model, row['name'])
+ future = run_one.submit(
+ model,
+ row,
+ gas,
+ )
+ futures.append(future)
+ except Exception:
+ continue
+
+ return [f.result(raise_on_failure=False) for f in futures]
+
+# run_all()
+run_all.with_options(
+ task_runner=DaskTaskRunner(address=client.scheduler.address),
+ log_prints=True,
+)()
diff --git a/benchmarks/mof/classification/input.pkl b/benchmarks/mof/classification/input.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..c64343235f1e28f359437e099c8251d65eaa7208
--- /dev/null
+++ b/benchmarks/mof/classification/input.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f66941e6898e0b0dbef120b2b17bd6828a1cf4db6aad94e1fd83aaa8b21acd84
+size 286336
diff --git a/benchmarks/mof/classification/mof-misclassification_margin.pdf b/benchmarks/mof/classification/mof-misclassification_margin.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..31cd95c7feb20efb342a2cc87826424769acd225
--- /dev/null
+++ b/benchmarks/mof/classification/mof-misclassification_margin.pdf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77b1c766a04fc365be40bff75e656d9d6bb9d39e10c282ef7e2e36ea97248138
+size 31870
diff --git a/benchmarks/mof/golddac.ipynb b/benchmarks/mof/golddac.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9f1002f615478673aa3a5721b9e1f35d6b31c33c
--- /dev/null
+++ b/benchmarks/mof/golddac.ipynb
@@ -0,0 +1,527 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Prepare input structures\n",
+ "\n",
+ "We first start by preparing the input ASE database. In the subfolder `golddac/test.xyz` there are a 312 total of MOF + gas configurations for 26 MOFs. The original `GoldDAC` dataset can be found at https://github.com/hspark1212/DAC-SIM/tree/main/assets/GoldDAC."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from dotenv import load_dotenv\n",
+ "\n",
+ "load_dotenv()\n",
+ "\n",
+ "from ase.io import read\n",
+ "\n",
+ "atoms_list = read(\"golddac/test.xyz\", index=\":\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "26\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Collect by MOF name\n",
+ "from collections import defaultdict\n",
+ "\n",
+ "mof_dict = defaultdict(list)\n",
+ "\n",
+ "for atoms in atoms_list:\n",
+ " mof_name = atoms.info[\"group\"]\n",
+ " mof_dict[mof_name].append(atoms)\n",
+ "print(len(mof_dict))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Demo Interaction (Adsorption) energy between MOF and gas molecules (CO2, H2O)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No module named 'deepmd'\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2025-01-10 16:24:46.439\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mmlip_arena.tasks.utils\u001b[0m:\u001b[36mget_calculator\u001b[0m:\u001b[36m30\u001b[0m - \u001b[1mUsing device: cuda:0\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MLIPEnum.MACE-MP(M)\n",
+ "Selected GPU cuda:0 with 40339.31 MB free memory from 1 GPUs\n",
+ "Selected GPU cuda:0 with 40339.31 MB free memory from 1 GPUs\n",
+ "Default dtype float32 does not match model dtype float64, converting models to float32.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2025-01-10 16:24:46.593\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mmlip_arena.tasks.utils\u001b[0m:\u001b[36mget_calculator\u001b[0m:\u001b[36m42\u001b[0m - \u001b[1mUsing calculator: \u001b[0m\n",
+ "\u001b[32m2025-01-10 16:24:46.640\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mmlip_arena.tasks.utils\u001b[0m:\u001b[36mget_calculator\u001b[0m:\u001b[36m54\u001b[0m - \u001b[1mUsing dispersion: \u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from mlip_arena.models import MLIPEnum\n",
+ "from mlip_arena.tasks.utils import get_calculator\n",
+ "from ase import units\n",
+ "\n",
+ "for model in MLIPEnum:\n",
+ " print(model)\n",
+ " break\n",
+ "calc = get_calculator(\n",
+ " calculator_name=model,\n",
+ " calculator_kwargs=None,\n",
+ " dispersion=True,\n",
+ " dispersion_kwargs=dict(damping='bj', xc='pbe', cutoff=40.0 * units.Bohr),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 0%| | 0/26 [00:00"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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uemEbB0sbPB2KiEi/5eXpAAaynNITrPuukCE+Bn59hdXT4YiISDdt2rSJd999lx07dnDs2DECAwOZMmUKd911F1OmTDnttW+99Rb33Xdfu+e+/vprIiIiXBFy2886UMzGgyVY6/y4+ly3fKSIyICjJMyDRkf44+NlpKK+iSMlJxg7bIinQxIRkW5YvXo1ZrOZG264gbFjx1JaWsoLL7zAtddey7PPPsucOXPO+B6PPPIIo0ePbnUsODjYRRG3NXGEbSzKLNGTMBERV1ES5kG+XiamRQWxPbuM1JwyJWEiIn3cH/7wB8LCwlodmz9/PhdccAFPP/10p5KwcePGMXXqVFeFeEYJMUEYDHD8hIXCiloiQ/w9FouISH+lNWEelhQbDEBqttmjcYiISPedmoAB+Pv7M2bMGI4ePeqBiJwX6OfN+GGBAOzIMXs2GBGRfkpPwjwsOS4ENh7WQCci0k9VVlayZ88eZs+e3an2S5YsobS0lMDAQFJSUvjZz35GfHx8lz7bYrF06brp0UPYd6yS1OwyLpoyvEvv0ZfZv29d/f71deq/+t/ydaDpbv87e52SMA9LbH4SduB4FeYT9QQP9vFsQCIi0qMeeughampqWLJkyWnbhYeHs2TJEqZPn05AQACZmZmsWrWKa6+9ltWrVzNhwgSnPzsjI6NLMUcYagD4Zn8B6VF1XXqP/qCr37/+Qv1X/wcyV/dfSZiHhdUXMNa/noPVPuzIMXPOhKGeDklERHrIP/7xD9577z1+//vfn7E64oIFC1iwYIHjzzNnzmThwoVceuml/POf/+Spp55y+vOnTp2KyWRy+rrAyApWbPuWLHMjE6dMw9drYK1esFgsZGRkdPn719ep/+q/+t/1/tuvPxMlYZ5UdgTjypn8w3sq3+O3bM8uVRImItJPPPHEEzz11FP84he/4LrrruvSe0RHR5OcnMzOnTu7dL3JZOrSLxGjIwIZ4mOgot7K3mNVtqnzA1BXv3/9hfqv/qv/ruv/wLq11QsZrBYmNnyHDw2kZpd5OhwREekBTzzxBCtWrODuu+8+4zTEM7FarRiN7h2uDQYD48Nt0+N35GhsEhHpaUrCPCk4DuugUEzWRiYYckjPNdNgafJ0VCIi0g0rV65kxYoV3HHHHSxdurRb75Wbm0taWhoJCQk9FF3nxYd5A+gGoYiIC2g6oicZDBCZCIc2MMs3m121Y9h7tIJp0cGejkxERLrg+eef5/HHH2f+/PmcffbZpKentzo/ffp0AO6//37Wrl3LJ598QlRUFAA33XQTM2bMYMKECfj7+5OZmcmzzz6LwWDgnnvucXNPYHyY7UlYWk4ZVqsVg8Hg9hhERPorJWEeZo1MxHBoAwv8c3imFrYfKVMSJiLSR33++ecAbNy4kY0bN7Y5v3//fgCampqwWCxYrVbHufj4eNatW8fzzz9PXV0doaGhzJ49mzvvvJNRo0a5pwMtjA3xxstooLCijnxzDdEhg90eg4hIf6UkzMOskUkATLIeBCA1p4xbcP9gKyIi3ffyyy93qt2yZctYtmxZq2P333+/K0LqMl8vAxNHBJKRX0FajllJmIhID9KaME+LTAQgtOYIg6klTXPvRUSkl7DvZamxSUSkZykJ87SAYdT7RWCwNjHNdISj5bXkm2s8HZWIiAhJsbbS9CrOISLSs5SE9QLVweMBuCAoH9BgJyIivUNSTDAAe45WcKK+0bPBiIj0I0rCeoETwRMASPE9AkDqkVIPRiMiImITGezHsCG+WJqs7Mor93Q4IiL9hpKwXsD+JGxUfSZgK84hIiLiaQaDwTElMU1jk4hIj1ES1gucaE7C/KtzCaaSvUcrqa7TtA8REfG85LjmJExT5UVEeoySsF7A4h2ANXQMAOcE5mNpsrIz1+zZoERERIAkexKWY261r5mIiHSdkrBewtpcqv68oDwAtuuOo4iI9AKTI4fgYzJSWl3PkZITng5HRKRfUBLWWzQnYdMMWYAqJIqISO/g62VianQQoCmJIiI9RUlYL2F/Ejaieg9gWwDd1KRpHyIi4nlJzZs2q3CUiEjPUBLWWwyfBgYT3ieOM9KnnMraRg4cr/J0VCIiIirOISLSw5SE9Rbeg2HoRAAuCz8GwPZs7RcmIiKeZy9Tv7+wksraBg9HIyLS9ykJ602apySeNSgb0LowERHpHYYO8SM6ZBBWK6Sreq+ISLcpCetNopIAGGc5ACgJExGR3sOxaXO22bOBiIj0A0rCepNIWxIWbN6NwWAlu+QERZV1Hg5KRETk5LowFecQEek+JWG9ybDJYPLFWGvm7DBbUY40DXYiItIL2J+E7VD1XhGRbuuzSdimTZu47777uOiii5g+fTrz58/njjvu4LvvvuvU9SUlJfzud79j1qxZJCQkcO2117Jp0yYXR30GJm8YPhWAC0MLAE1JFBGR3mHCiEAGeZuorG3kYJGq94qIdEefTcJWr15Nfn4+N9xwA6tWreKBBx6gtLS0U8lUfX09N910E5s2beKBBx7gySefJCwsjFtvvZWtW7e6qQcdaF4Xlux1GFASJiIivYO3yUhCjDZtFhHpCV6eDqCr/vCHPxAWFtbq2Pz587ngggt4+umnmTNnTofXrlmzhszMTP7zn/+QmGirSDhr1iwuv/xyli9fzpo1a1wa+2k1rwuLqdkHXERGXjm1DRb8vE2ei0lERATblMTNWaWkZpfxw5RYT4cjItJn9dknYacmYAD+/v6MGTOGo0ePnvbaTz/9lFGjRjkSMAAvLy8uu+wydu3aRWFhYY/H22nNT8J8i79jmL+JeksTuwvKPRePiIhIM8emzVqvLCLSLX32SVh7Kisr2bNnD7Nnzz5tuwMHDpCcnNzm+Pjx4x3nhw0b5tRnWywWp9qfep3j+pDRGH0CMNRXcXFUOS8eCmDr4VKmRwd16f17uzb9H2DUf/W/5etA093+D9TvmyclNhfnOFRUjflEPcGDfTwckYhI39SvkrCHHnqImpoalixZctp2ZrOZoKC2SY39mNlsdvqzMzIynL6mo+vjA8cSWJJOQuN3wGw+33WElMD+/TSsu9+/vk79V/8HsoHe/74k1N+H0eH+ZBVXsyPHzDkThno6JBGRPqnfJGH/+Mc/eO+99/j973/PlClTztjeYDB06VxHpk6disnk/Loti8VCRkZGq+sNxfNhUzpzg4oBOFhuJSEhoUtx9Xbt9X8gUf/Vf/W/6/23Xy/ulRgbQlZxNanZZUrCRES6qF8kYU888QRPPfUUv/jFL7juuuvO2D44OLjdp13l5banTe09JTsTk8nUrV+iWl0fbZsqGVG5Bx/TZZRW15NnrmNkuH+X37+36+73r69T/9V/9X/g9r+vSY4L4c20PK0LExHphj5bmMPuiSeeYMWKFdx9991nnIZoFx8fT2ZmZpvj9mPjxo3r0Rid1lwh0Vi4m8SoQQBsVzlgERHpBZLiggFIzzXTaGnybDAiIn1Un07CVq5cyYoVK7jjjjtYunRpp69btGgRWVlZ7Ny503GssbGRd999l4SEBKeLcvS44FgYHAZNDVwcbpuSqP3CRETELbK+wPjYeIKOfdPu6XFDAwn09eJEvYV9xyrdHJyISP/QZ5Ow559/nscff5z58+dz9tlnk56e3uo/u/vvv59JkyaRn5/vOHb11Vczbtw47rnnHt577z2+/fZbfv7zn3P48GF+/etfe6A3pzAYHE/DUnyzAUjNLvVkRCIiMlAU7sFQXUR4zvp2T5uMBqbHBgOwQ1MSRUS6pM+uCfv8888B2LhxIxs3bmxzfv/+/QA0NTVhsViwWq2Ocz4+Prz44ossX76chx9+mJqaGiZOnMgzzzxDSkqKezpwJlFJcPATRtfvByaQWVhFeU0DQYO8PR2ZiIj0Z1G2dcn+ZbuhxdjZUlJsCBsPFJOaXcb1c0a6MTgRkf6hzyZhL7/8cqfaLVu2jGXLlrU5Hh4ezqOPPtrTYfWc5idhfsd3MjLsRxwpOUFaThnnjFclKhERcaERCViN3njXm7GUHYGIsW2anNy02eze2ERE+ok+Ox2x34uyJWEU7WdutB8AaVoXJiIirubtByOmAWDI29Zuk+mxwRgMkFN6gqLKOndGJyLSLygJ660ChsKQaMDKuUEFgIpziIiIe1ijZ9q+yG8/CRvi50380EAAlaoXEekCJWG9WVQiAAmmLEDlgEVExE2ibElYR0/C4GSpes3SEBFxnpKw3qx5XVh4+W4C/VQOWERE3MMa01ykqnA31FW12yYp1r4uTEmYiIizlIT1Zs3rwgwFaY7BbvsRlaoXEREXGxJFvV8EBqsFCtLabZLUXJxjZ1459Y2apSEi4gwlYb3ZiOm2V3M28yINAKSqEpWIiLhBVcgk2xe5W9s9Pzrcn+DB3tQ3NrHnaIUbIxMR6fuUhPVmg4IhzFYa+KzBOQCk6kmYiIi4QbU9CetgXZjBYHDM0lDhKBER5ygJ6+2a14WNbTiAyWigoLyWAnONh4MSEZH+rjp0su2LvG0dbtrs2C9MSZiIiFOUhPV2zevCfArTmTjCVg5YdxxFRMTVTgwZi9XkAydKoDSr3TaJscGAinOIiDhLSVhv1/wkjII0Zmjah4iIuInV5HNybXIH68ISooMxGQ0c1SwNERGnKAnr7YZPBYMJqgqZO6weUBImIiLu4di0Oa/9JMzf18sxS0NPw0REOk9JWG/nMxiG2hZHz/A6DMCeoxWcqG/0ZFQiItKOTZs2cd9993HRRRcxffp05s+fzx133MF3333XqetLSkr43e9+x6xZs0hISODaa69l06ZNLo66Y44krIMnYYCKc4iIdIGSsL4gKhGAUPN3jAjyw9JkJT3X7NmYRESkjdWrV5Ofn88NN9zAqlWreOCBBygtLe1UMlVfX89NN93Epk2beOCBB3jyyScJCwvj1ltvZevWjpMgl7InYcf3QF1lu00cxTm0hYqISKd5eToA6YTIJEh7CQrSSI67lPd3HSUtu4y5Y8I9HZmIiLTwhz/8gbCwsFbH5s+fzwUXXMDTTz/NnDlzOrx2zZo1ZGZm8p///IfERNvNt1mzZnH55ZezfPly1qxZ49LY2xU4AoJioDwX8lNh9NltmtifhO3OL6e2wYKft8nNQYqI9D16EtYXRNmLc+wgubkS1XZN+xAR6XVOTcAA/P39GTNmDEePHj3ttZ9++imjRo1yJGAAXl5eXHbZZezatYvCwsIej7dTHFMS298vLDpkEBGBvjQ2WcnIL3djYCIifZeehPUFQyeBlx/UljM3pAKw7cnS1GTFaDR4ODgRETmdyspK9uzZw+zZs0/b7sCBAyQnJ7c5Pn78eMf5YcOGOfXZFovFqfanXmexWDBEz8S4+y2suVto6uD9EmOC+XhPIdsOl5AUE9Slz+xNWvZ/IFL/1f+WrwNNd/vf2euUhPUFJm9blcS8bYxp2M8g7yAqahs5VFTFuGGBno5ORERO46GHHqKmpoYlS5actp3ZbCYoqG0CYz9mNpud/uyMjAynrzn1+sEngpgIWLK3sHNHGhjaTqIZ7nUCgC8yspk1pKJbn9mbdPf719ep/+r/QObq/isJ6ysikyBvG17H0pkeczmbskrYnl2mJExEpBf7xz/+wXvvvcfvf/97pkyZcsb2BkPHsxtOd64jU6dOxWRyfo2WxWIhIyPDdj2TsG76JV4NFUyPCYDw+DbtG0PKeGnXFg6VW0lISOhSrL1Jq/534fvX16n/6r/63/X+268/EyVhfYV9XVh+GslxN7Epq4TU7DJ+lBLr2bhERKRdTzzxBE899RS/+MUvuO66687YPjg4uN2nXeXltnVW7T0lOxOTydStX6Js1/tAZCLkbMJUkArDJrZplxATgrfJQEl1PfnldcSF+Xf5M3uT7n7/+jr1X/1X/13XfxXm6Csim5OwozuZEWN7+qU9WUREeqcnnniCFStWcPfdd59xGqJdfHw8mZmZbY7bj40bN65HY3TKGfYL8/M2MTnSliRq02YRkTNTEtZXhI0F3yHQWEPy4OMAHC6upqSqzsOBiYhISytXrmTFihXccccdLF26tNPXLVq0iKysLHbu3Ok41tjYyLvvvktCQoLTRTl6VEyK7TWv/QqJcHK/MN0gFBE5MyVhfYXRCCMSAAgs3cW4oQGABjsRkd7k+eef5/HHH2f+/PmcffbZpKent/rP7v7772fSpEnk5+c7jl199dWMGzeOe+65h/fee49vv/2Wn//85xw+fJhf//rXHuhNC9HNSdjxvVDbfhl6x6bN2WY3BSUi0ndpTVhfEpUERzZCfhozRk7nwPEqUnPKuGDycE9HJiIiwOeffw7Axo0b2bhxY5vz+/fvB6CpqQmLxYLVanWc8/Hx4cUXX2T58uU8/PDD1NTUMHHiRJ555hlSUlLc04GOBA6D4DgwZ9s2bR5zbpsm9k2b9x2roKqukQBf/YohItKRLv0LeeDAAdLS0igsLKS2tpaQkBDGjh3LzJkzCQgI6OkYxc6+LqwgjaTkX7F6ay6pR/QkTESkt3j55Zc71W7ZsmUsW7aszfHw8HAeffTRng6rZ8Sk2JKw3K3tJmHDg/yICh5EvrmGXblm5o4N90CQIiJ9Q6eTsPLycl5//XVef/11CgoKWt29c7yZlxcLFizg+uuvZ86cOT0aqHCyQmLhbmZGDwZgV345dY0WfL0GbvUaERFxg+gUyFjTYXEOgMTYYPLNNaRmlykJExE5jU4lYS+99BIrV64E4JJLLiElJYXJkycTGhqKr68v5eXl5Obmkp6ezoYNG7jllluYO3cu//u//0tcXJxLOzCgBMXA4HA4UUxcQxZh/j6UVNfzXX6FYy6+iIiIS8Q0V0jM2w5NTba1yqdIjgvh/V1HVSFRROQMOpWEvfzyy9x3330sXrwYb2/vNufDw8MJDw8nMTGRm2++mZycHJ566inWrVvX6dK80gkGg+1p2IGPMRTsICkuiU/2FJKWXaYkTEREXGvYFPAaBHXlUJwJQye0aWJfF5aWY6apyYrR2Lc3bRYRcZVOVUdct24d3//+99tNwNoTGxvLI488wk9/+tNuBSftaLEuzJ54bc8u9WBAIiIyIJi8T06Lz2t/SuKkyCH4eRspr2kgq7jajcGJiPQtnUrCDhw40KU3H8i7bLuMfQDMT2OGY08Wc7tr9ERERHqUfb+wDtaFeZuMTIsKBiBNW6iIiHSoU0nYFVdcwZVXXslrr71GZWWlq2OS07E/CSvOZEq4ER+TkeKqOnJKT3g2LhER6f+iz7xpc5J9vzCtCxMR6VCnkrDbb7+d0tJS/vSnPzFv3jx+/etfs3nzZlfHJu0JiLAV6MCKX1EGU6KGANq0WURE3CC6uThH0T6oaX/cSXbM0tC4JCLSkU4lYb/4xS/4/PPPWbVqFeeccw4ff/wxN998M+eddx5PPvkkR48edXWc0lJkou21xbowDXYiIuJyAREQMsr2dV5qu00SY4MBOHC8ivITDW4KTESkb+lUEgZgMBhYsGAB//jHP/j666954IEHCAoK4vHHH2fRokX8z//8D+vXr6ehQf/gulyLdWHJcaGAkjAREXET+7qwDopzhAf4MjLMtpfljlyNTSIi7el0EtbSkCFDuO6663jrrbdYu3YtP/7xj9mzZw+/+MUvWLBgQU/HKKdqUSExKS4YgP2FlVTUKgEWEREXs09JPM2mzY5S9bpBKCLSri4lYS1NmDCByy67jHPPPRcAs9nc3beUM4mcbns15zDUWEVs6GCsVtiRY/ZkVCIiMhDEzLK95qfaNm1ux8niHGY3BSUi0rd0arPm9pSWlvLuu+/y5ptvcvDgQUwmE+eccw5XX311T8Yn7fELgrBxUHIACnYwIy6CnNITpGaXsTA+wtPRiYhIfzZ0Enj7Q12FrUDHsEltmtifhO3IKcPSZMWkTZtFRFpxKglramriq6++4s033+SLL76goaGBkSNH8stf/pIrrriC8PBwV8Upp4pMtCVh+Wkkxf2Qt3bkk6pNm0VExNVMXra1yUc2Qu6WdpOw8cMD8fcxUV1vIbOwkokjhnggUBGR3qtTSdjhw4d58803eeeddyguLsbPz4/vfe97XHXVVcyYMcPVMUp7opIg4w0oSGPGuXcAkJ5jptHShJep27NMRUREOhaTYkvC8rbBjJvbnDYZDUyPDeabgyWkZpcpCRMROUWnkrCLL74YgGnTpnH33XezePFi/P39XRqYnEHkyQqJ4yICCPT1orKukX3HKpkSFeTZ2EREpH+zb9p8muIcybEhfHOwhLScMq6bHeemwERE+oZOJWE33ngjV199NePGjXN1PNJZw6eCwQTVxzFVFZAYF8JXmUWk5ZQpCRMREdeyV0gsOQAnSmFwaJsmiXGqkCgi0pFOzVu777772k3AsrKySE1N5cSJEz0emJyBz2Db4miw7RfWvAh6+xENdiIi4mL+YRA6xvZ13vZ2myTF2MalIyUnKKmqc1dkIiJ9QpcWD61du5YFCxawePFirrvuOg4fPgzAPffcwxtvvNGjAcppRCXaXgvSmDHSNthp02YREXELe6n6DjZtDhrszbihAYBK1YuInMrpJGzdunX87ne/Y9KkSfz+97/HarU6zk2ePJl169b1aIByGi3WhSXEBGM0QL65hmPltZ6NS0RE+r+Yzm/arBuEIiKtOZ2ErVq1iiuvvJJ//etfXHvtta3OjR49moMHD/ZYcHIGUc1JWEE6Ad5GR/UpDXYiIuJy9uIc+anQZGm3SbJj02aNSyIiLTmdhB06dIjFixe3ey44OBiz2dzdmKSzhk4CLz+oK4fSLMdgt137hYmIiKsNnQg+gVBfBcf3tNskKS4YgF15ZhosTW4MTkSkd3M6CRs0aBCVlZXtnissLCQoSJX53MbkbauSCFCQdvKOo56EiYiIqxlNJ2dkdDAlcXR4AEGDvKltaGLv0Qo3Bici0rs5nYQlJiby6quvtloLZvfWW2+RkpLSI4GdSVVVFX/5y1+45ZZbmD17NuPHj2fFihWduvatt95i/Pjx7f5XVFTk4sh7WIt1YfYkbHdBBTX17U8NERER6TExzWN+3rZ2TxuNBhJjgwFNlRcRaalT+4S1dNddd/HjH/+Yq6++mksvvRSDwcDHH3/MihUr2L59O2vWrHFFnG2YzWbeeOMNJkyYwKJFi7r0uY888gijR49udSw4OLiHInQTx7qwNKKCBzF8iB/HKmrZmWdm9ugwz8YmIiL9m71C4hk2bf5ifxFpOWZuPstNcYmI9HJOJ2FTp07lmWee4aGHHmLZsmUAPP3008TFxbFq1Sri4+N7PMj2REVFsW3bNgwGA6WlpV1KwsaNG8fUqVNdEJ0b2Z+EHd2FoclCclwIH2QcJTW7TEmYiIi4VvQM22vpIaguse0fdookTZUXEWnD6SQMYPbs2axbt46cnByKi4sJCQlh1KhRPR3baRkMBrd+Xq8VNhZ8h0BdBRTtJalFEiYiIuJSg0IgPB6KM237hY2/uE2TU7dQGR7k54FARUR6ly5t1mwXGxtLUlKS2xOwnrJkyRImTpxISkoKS5cuJTMz09MhOc9ohBEJtq/z05jRohxwU1PbdXsiIiI9yl6qvoMpiQG+XowfbttCRaXqRURsOvUk7MMPP+SSSy5x6o0LCwvJy8sjOTm5S4G5Unh4OEuWLGH69OkEBASQmZnJqlWruPbaa1m9ejUTJkxw+j0tlq4VwrBf19XrAQwjEjEe2UhTfirjp/4YP28j5hMNHCisYOzQgC6/rzv0RP/7MvVf/W/5OtB0t/8D9fvW68TMhPRXOizOAZAcF8zeoxWkZZdxydQRbgxORKR36lQS9qc//Ymnn36a6667josvvpiAgI5/sf/uu+948803efvtt/nNb37TK5OwBQsWsGDBAsefZ86cycKFC7n00kv55z//yVNPPeX0e2ZkZHQrpu5cH1wfwhig9uA37M3YxZhgE7uLmlj7zS4WjRrcrbjcpbvfv75O/Vf/B7KB3v8+r+WmzZZGMLX91SI5LoRXNueQqidhIiJAJ5OwTz75hBUrVvDnP/+ZP/3pT0yaNIlJkyYRFhaGj48P5eXl5Obmkp6eTlFREePGjWPFihXMnz/f1fH3mOjoaJKTk9m5c2eXrp86dSomk8np6ywWCxkZGV2+HoCRYZD6EIOqjjB9ygQWHM9h95dZFDUFMn167y480iP978PUf/Vf/e96/+3Xi4dFTDi5Nvn47pNT5FtIirVNlf8uv5zaBgt+3gPv77uISEudSsICAwO5//77ueuuu3jrrbf48ssvWbt2LTU1NY42MTExzJ8/n0svvZTZs2e7LGBXslqtGI1dWyZnMpm69UtUt64PjYPB4RhOFGM6voeZo2J56sss0nLNfeYXu+5+//o69V/9V/8Hbv/7PKPRViXx0Ge2dWHtJGGxoYMJD/ChuKqe3QXlJMeFeiBQEZHew6nqiEFBQdx8883cfPPNAFRWVlJbW0twcDDe3t4uCdBdcnNzSUtLY+7cuZ4OxXkGg22/sAMfQ0EaSVNtA2BWUTWl1fWE+vt4OEAREenXolNOJmEpP21z2mAwkBgbwid7CknNLlMSJiIDXpdK1NsFBgYSGBjYU7E47csvv6Smpobq6moADh48yPr16wFYuHAhgwYN4v7772ft2rV88sknREVFAXDTTTcxY8YMJkyYgL+/P5mZmTz77LMYDAbuuecej/WnWyKbk7D8NIJn3c7YoQEcPF5FWnYZiyYN83R0IiLSn8XMtL3mnWbT5jhbEpaWbXZPTCIivVi3kjBPe+ihh8jPz3f8ef369Y4kbMOGDURHR9PU1ITFYsFqPVmuPT4+nnXr1vH8889TV1dHaGgos2fP5s477+yz5faJat60uSANgOTYEA4er2K7kjAREXG1qOZNm8uOQFURBES0aWJfF5aaU4bVatV+nyIyoPXpJOyzzz47Y5tly5axbNmyVsfuv/9+V4XkOZHNSVjxAaitIHlkCK9vzyVNmzaLiIirDQq2Fego2md7GjZhcZsm06KD8DIaKKqsI6+shpjQvlG9V0TEFbq1WbP0IgEREBQDWOFoOsnNmzbvzDNT39jk2dhERKT/i26ektjBps1+3iYmR2rTZhERUBLWv0Qm2l7z0xgd7k/IYG/qGpvYXVDu2bhERKT/i5llez3Nps1JzTcINUtDRAY6JWH9SYt1YQaDwfE0LFWDnYiIuFqMfdPmNLA0tNuk5bowEZGBzOkk7P/+7//IyspyRSzSXfZ1Yfk7ABwlgJWEiYiIy4WNA78gaKyBY+1vom2/Obj3aCUn6hvdGZ2ISK/idBK2du1aFi9ezM0338ynn37aquqgeFjkdNtreQ5UFzsGu+3ZZfo5iYiIaxmNJ9eFdTAlMTJ4ECOC/LA0WdmZq6nyIjJwOZ2Ebdy4kQcffJCioiKWLl3Kueeey6pVqygtLXVFfOIMvyDbnUiA/DSmRQfhbTpZiUpERFyrqqqKv/zlL9xyyy3Mnj2b8ePHs2LFik5d+9ZbbzF+/Ph2/ysqKnJx5D0kunlKYgfFOeDklEQV5xCRgczpJGzw4MH85Cc/4f333+eFF15g8uTJ/POf/+Tss8/md7/7HRkZ7U9BEDdpsS7MVokqCNCURBERdzCbzbzxxhvU19ezaNGiLr3HI488wuuvv97qv+Dg4J4N1FU6sWmzinOIiHRzn7A5c+YwZ84cjh07xr333ss777zDO++8w5QpU7jjjjs499xzeypO6azIJNj1um1hNLb59+m5ZrZnl/L9xCgPByci0r9FRUWxbds2DAYDpaWlrFmzxun3GDduHFOnTnVBdG4QNQMwgDkHKgshcFibJkmxwYDtSZg2bRaRgapb1RFra2tZs2YNS5YsYcuWLYwZM4a77roLi8XCXXfdxcqVK3sqTumsFk/CsFqZ4aiQaPZcTCIiA4TBYBjYSYXfEBg6yfZ1B0/DJkcG4eNlpOxEA4eLq90YnIhI79GlJCwnJ4dHHnmEBQsW8Ic//IHhw4fz/PPP8/7777N06VLeeustfvrTn/LKK6/0dLxyJsOngtELqougPM9RnGP/sQoqa9svGSwiIr3HkiVLmDhxIikpKSxdupTMzExPh+Qc+5TE3C3tnvbxMjItSlPlRWRgc3o64q233sq3337LoEGDuPLKK7n++uuJjY1t0+6cc85h1apVPRKkOMF7EAydaCsPXJDG0EmXExM6iNzSGtJzzcwfF+HpCEVEpB3h4eEsWbKE6dOnExAQQGZmJqtWreLaa69l9erVTJgwwen3tFgsXYrFfl1XrjdEzcCY+iLW3K00dXB9Ymww27PLSM0u5crEyC7F6Erd6X9/oP6r/y1fB5ru9r+z1zmdhOXm5nLfffdx5ZVX4u/v32G7cePG8dJLLzn79tITIpOak7AdMOlykmNDyC2tYfuRMiVhIiK91IIFC1iwYIHjzzNnzmThwoVceuml/POf/+Spp55y+j27WyyrK9f7VgUwBbDmp7EzbRtWo3ebNiGWWgC+3X+M9PTeu1/YQC82pv6r/wOZq/vvdBL20UcfdapdQEAAKSkpTgckPSAqCdL+fbI4x8hQ1qYXqBywiEgfEx0dTXJyMjt37uzS9VOnTsVkMjl9ncViISMjo2vXWxOwbg7BWFNGwjAjRE1v0yRqTB1/+fZzcisbGTNhMoF+bRM1T+pW//sB9V/9V/+73n/79WfSreqI0ktF2otzpENTE8nNe7LsyDFjabJiMg7gReMiIn2M1WrFaOxaHS2TydStX6K6fH10Chz4CFNBKsS2vSE7PHiwY6r8rvxKFsT3zlka3f3+9XXqv/qv/ruu/04nYeeee26HlZ+MRiOBgYFMnTqVG264gTFjxnQ7QOmCoRPByw/qyqE0i/HDxxDg60VVXSP7j1UyKXKIpyMUEZFOyM3NJS0tjblz53o6FOfEzIQDH9k2bZ59R7tN7FPl03LKem0SJiLiKk7fWktJScFqtVJYWEhUVBQJCQlERkZSWFiIxWJhxIgRfPLJJ1x11VUDfi6px5i8Yfg029cFaZiMBhKb92VJzS71XFwiIgPAl19+yfr16/n8888BOHjwIOvXr2f9+vXU1NQAcP/99zNp0iTy8/Md191000088cQTfPrpp2zatIl///vf/PjHP8ZgMHDPPfd4pC9dFt389CtvW4dNkh1bqGiqvIgMPE4/CZs3bx7p6el88sknjBgxwnG8oKCAW265hUWLFrFs2TKuv/56VqxYoQqJnhKVZNujJT8Npv2A5LgQNh4oJjW7jOvnjPR0dCIi/dZDDz3UKrmyJ2AAGzZsIDo6mqamJiwWC1ar1dEuPj6edevW8fzzz1NXV0doaCizZ8/mzjvvZNSoUW7vR7dEJYPBCOW5UFEAQ9pWQExsniqfnmOmqcmKUVPlRWQAcToJ+9e//sXdd9/dKgEDiIyM5K677uLJJ5/kiiuu4KabbuL//b//12OBipMiW2zaTIs7jirOISLiUp999tkZ2yxbtoxly5a1Onb//fe7KiT38w2AoZOhMMM2JXHy99s0mTA8kME+JirrGjlwvIrxwwPdH6eIiIc4PR0xOzubgICAds8NGTLEcfcvKirKMe1CPCCqOQk7ugssjUyPCcZogNzSGo5X1Ho2NhER6f/smzZ3MCXRy2QkIToY0JREERl4nE7CIiMjefvtt9s99+abbzqekJnNZoKCgroXnXRd6BjwHQKNNVC0l0A/b8YPtxXk0GAnIiIuFzPL9pq7tcMm9lka2kJFRAYap5Ow//mf/2H9+vX88Ic/5MUXX+T999/nxRdf5Ic//CGffPIJt956KwBbtmxhypQpPR6wdJLRCJHTbV837xc2o3mw264kTEREXC26+UnY0XRorGu3SVJcMABpGpdEZIBxek3YD37wA6xWKytWrGg1nz08PJyHHnqIa665BoAlS5bg4+PTc5GK8yKT4PBXtnVhyTeSHBfCy5uz9SRMRERcL3Q0DA6DEyW2qfH26YktJMbYbg5mFVdTWl1PqL9+bxCRgcGpJMxisZCTk8PFF1/MD37wA7KysjCbzQQHBzN69OhW+4eFh4f3eLDiJPu6sPzWxTl2F5RT22DBz3vgbsAnIiIuZjDYStVnroPcLe0mYSH+PoyO8CerqJodOWWcN3GYBwIVEXE/p6YjWq1WFi9ezI4dOzAYDIwZM4bk5GTGjBnT4QbO4kH2ConH90BDLdEhgxga6EuDxcquvHLPxiYiIv2fozjHadaFxWpdmIgMPE4lYV5eXoSHh7fa10R6saBo8I+ApkY4loHBYHA8DduuTZtFRMTV7Js253a8aXOSNm0WkQHI6cIcixcvZu3atS4IRXqcwdDhfmFaBC0iIi4XlQQGE1QWQHleu03s49LO3HIaLU3ujE5ExGOcLswxYcIEPvzwQ2644QYuuOACIiIi2kxFvOCCC3osQOmmqCQ48FGbdWGp2WVYrVZNIxUREdfx8YfhU+DoTlup+qDoNk3GRgQQ6OdFZW0j+45VMiVK29uISP/ndBJ27733AlBYWMjWrW3neBsMBvbu3dv9yKRnnPIkbHJkEL5eRspONJBVXM2YiPY33hYREekR0Sm2JCxvG0y5ss1po9FAYmwIX2UWkZZTpiRMRAYEp5Owl156yRVxiKvYKyQWH4DaCnz8hpAQHczWI6WkHilTEiYiIq4VkwLbnrFVSOxAcnMSlppdxg1zRrovNhERD3E6CUtJSXFFHOIq/uEQFAvlObYNM0ctIHlkiC0Jyy7jBzNjPB2hiIj0Z45Nm3dBQy14+7VpYt+0WcU5RGSgcLowh11lZSUbN27k3Xffpbxc5c57tahE26t9XVisKiSKiIibhIxsrtTbYLsZ2I7pMcEYDJBXVsPxilq3hici4gldSsJWrlzJ/Pnz+elPf8q9995LXp6t4tGNN97IqlWrejRA6QGnrAuzlwM+VFRNWXW9p6ISEZGBwL5pM9iKc7Qj0M+b8cMCAe0XJiIDg9NJ2KuvvsrKlSu5+uqrefrpp1vtGXbOOefwxRdf9GR80hPs68LydwAQ6u/D6Ah/AHbkarATEREXi2lOwk6zabP9BmFajtkNAYmIeFaXkrCbbrqJBx98kHnz5rU6FxcXR3Z2do8FJz1kxHTAYFsXVl0MwAz7ps1HlISJiIiLxbTYtLnFzduWkmK1abOIDBxOJ2G5ubnMnz+/3XP+/v5UVFR0OyjpYX5DIHyc7et29gsTERFxqchEMHpB1TEoz223iX1cysgvp67R4s7oRETczukkLDAwkOLi4nbP5efnExYW1u2gxAVOWReWHBcKwM48Mw2WJk9FJSIiA4H3IBg+1fZ1B+vCRoYNJtTfh/rGJnYX6IauiPRvTidhc+bM4dlnn+XEiROOYwaDgcbGRlavXt1miqL0Eo51YbYkbHS4P8GDvaltaGKPBjsREXG1MxTnMBgMJMUGA5CmWRoi0s85nYT97Gc/o6CggMWLF7Ns2TIMBgOvvPIK11xzDdnZ2dx5552uiFO6q+WTMKsVo9HgmH+/XYOdiIi4mlPFOTQuiUj/5nQSFhcXx+rVqxk9ejSrV6/GarXyzjvvEBISwmuvvUZkZKQr4pTuGj7VNh+/ugjKbVsK2Off646jiIi4nD0JO5YBDTXtNmlZnMPaQQEPEZH+wKsrF40dO5bnnnuO+vp6ysrKCAoKws/Pr6djk57k7QdDJ8GxXbanYcExjiRse3YpVqsVg8Hg4SBFRKTfCoqBgOG24hwFOyBubpsmCdHBmIwGCivqKCivJSp4kAcCFRFxvS5t1mzn4+PDsGHDlID1FaesC0uIDsarebDLN7d/V1JERKRHGAwQM9P2dQfrwgb5mJg0YgigWRoi0r916UlYXl4e69ato6CggNra2lbnDAYD/+///b8eCU56WGQSpL7oqJA4yMfE5Mgh7MwrJzW7jOiQwZ6NT0RE+rfoFNj7XodJGNimymfk28alSxO0xEFE+ienk7AvvviCpUuX0tTURGhoKD4+Pq3Oa0pbL2Z/ElaQDk1NYDSSHBfqSMIunx7l0fBERKSfa1mcw2q1PR07RWJsMC9+q+IcItK/OZ2E/f3vfycpKYm///3v2hOsr4mYCF6DoK4CSg9B+DiS40J4/pvDbD+iwU5ERFxsxHQwetuKRJUdgdBRbZrY1yvvKaigpt7CIB+Te2MUEXEDp9eEZWdn89Of/lQJWF9k8oIR02xfN68LmzHSNtjtO1ZBVV2jpyITEZGBwNvv5DiUt63dJlHBgxga6Etjk5VdeWb3xSYi4kZOJ2GRkZGtNmqWPqblfmHAsCF+RAUPoskK6Tlmz8UlIiIDQ8ws2+tpNm12bKGicUlE+imnk7Dbb7+d559/npoaVdPrk06pkAgnn4alqhKViIi4WnRzhcTTbdocq3FJRPo3p9eEZWRkUFJSwvnnn8+sWbMICQlp0+bBBx/skeBOp6qqiieffJJ9+/axZ88eysrKWLp0KXfffXenri8pKWH58uV8/vnn1NbWMmHCBH7+858zZ84cF0fuYfYnYcd2gaUBTN4kx4XwTnoBqVoELSIirubYtPk7qK8GH/82TZKan4TtyCnTPpYi0i85nYS98sorjq8/+OCDNucNBoNbkjCz2cwbb7zBhAkTWLRoEWvWrOn0tfX19dx0001UVFTwwAMPEBYWxquvvsqtt97KCy+8QEpKigsj97DQ0eAbBHXlcHwvjJjmmPaxI7sMS5MVk1GDnYiIuEhQNARGQmWBbVbGqPltmkyJGoKPyUhJdT3ZJScYGd42URMR6cucTsL27dvnijicFhUVxbZt2zAYDJSWljqVhK1Zs4bMzEz+85//kJiYCMCsWbO4/PLLWb58uVPv1ecYjRA5HQ5/aVsXNmIa44cF4u9jorKukQPHK5kwfIinoxQRkf4sZibsecc2JbGdJMzXy8SUqCGk5ZhJyylTEiYi/Y7Ta8J6C4PB0OXpCZ9++imjRo1yJGAAXl5eXHbZZezatYvCwsKeCrN3OmVdmJfJSGLz/HuVqhcREZeLbp5xktt+hUQ4Wape68JEpD/qVBK2bds2qqurz9iutLSU//73v90OytUOHDjA+PHj2xy3Hztw4IC7Q3KvUyokwsn592ka7ERExNXsFRLtmza3w16cQxUSRaQ/6tR0xBtuuIHXX3+dadNse3s0NTUxbdo03njjDSZNmuRol5uby+9//3uuvvpq10TbQ8xmM0FBQW2O24+ZzWan39NisXQpFvt1Xb2+S4YnYAKshXtoqq0C70EkxtimIG7PLnVrLB7pfy+i/qv/LV8Hmu72f6B+3/qFEdPA5AMnSqA0C8LGtGlivzm4/1gFlbUNBPp5uztKERGX6VQSZj3lLpXVaqWxsbHN8b7kdFMZuzLNMSMjozvhdPt6p1itTPMNwbuujAMb36I6dDKmhiYMQE5pDZ9vTiXEz+S+eHBz/3sh9V/9H8gGev8HJC9fGDHd9iQsb1u7SZh9H8t8cw07c8uZNy7c/XGKiLiI04U5+oPg4OB2n3aVl5cDtPuU7EymTp2KyeR84mKxWMjIyOjy9V1l3J8CBz4iPqAK6/TpAMRv+pr9hVXUB0YxffJwt8Thqf73Fuq/+q/+d73/9uulj4pJsSVhuVsg4YftNkmOCyHfXENaTpmSMBHpVwZkEhYfH09mZmab4/Zj48aNc/o9TSZTt36J6u71TotKhgMfYTyaDs2fO2NkKPsLq9iRW84l06LcFwse6H8vo/6r/+r/wO3/gGXftPk0xTmSYoN5d2eBinOISL/TZ6sjdseiRYvIyspi586djmONjY28++67JCQkMGzYMA9G5yanVEiEk5WotmuwExERV7Nv2nx8N9RVttskOS4UsG3a3NTUd5dAiIicqtNPwrKyshx3Ku2LobOystq0cacvv/ySmpoaR+XGgwcPsn79egAWLlzIoEGDuP/++1m7di2ffPIJUVG2pztXX301r732Gvfccw+/+tWvCAsL47XXXuPw4cO88MILbu2Dx9grJJYcgNpy8AtiRvNg911+ObUNFvy8dWdaRERcZEgkBMVAea7thuDohW2aTBgRiJ+3kYraRg4VVTFuWKAHAhUR6XmdTsLuu+++Nsd++9vftvqz1Wrt8t5dXfHQQw+Rn5/v+PP69esdSdiGDRuIjo6mqakJi8XSqoiIj48PL774IsuXL+fhhx+mpqaGiRMn8swzz5CSkuK2+D3KPwyCY8GcAwXpMHohMaGDCA/wpbiqjoz8cmaODPV0lCIi0p9Fz7QlYXlb203CvE1GEqKD2XK4lLScMiVhItJvdCoJe+SRR1wdR5d89tlnZ2yzbNkyli1b1uZ4eHg4jz76qCvC6jsik5qTMNsdSIPBwIy4ENbvPkZqdpmSMBERJ1VVVfHkk0+yb98+9uzZQ1lZGUuXLuXuu+/u1PUlJSUsX76czz//nNraWiZMmMDPf/5z5syZ4+LIPSQmBXa/dcZNm7ccLiU1u4xrZ8a6MTgREdfpVBJ2xRVXuDoO8YSoJNizts26MHsSJiIizjGbzbzxxhtMmDCBRYsWsWbNmk5fW19fz0033URFRQUPPPAAYWFhvPrqq9x666288MIL/XOmRnRzn+ybNrczm0abNotIfzQgqyNKM/u6sIIdjkPJI5sHu+wyt08vFRHp66Kioti2bRsGg4HS0lKnkrA1a9aQmZnJf/7zHxITEwGYNWsWl19+OcuXL3fqvfqM4VPByw9qyqDkIIS3rU5s37T54PEqzCfqCR7s4+4oRUR63ICsjijNIqcDBtt8/KoiACZHDsHHy0hJdT1HSk54NDwRkb7GYDB0+ebVp59+yqhRoxwJGICXlxeXXXYZu3btorCwsKfC7D28fGybNgPkbm23Sai/D6PC/QHYkWt2T1wiIi6mJ2EDmW8ghMdD8X7burD4C/H1MpEQHcS2I2VsP1LqGPhERMS1Dhw4QHJycpvj48ePd5x3dgsVezVjZ9mv6+r1zjBEz8CYu5mm3C1Yp7W/aXNiTBCHi6vZfriUBWPDXB6TO/vfG6n/6n/L14Gmu/3v7HVKwga6qCRbEpZvS8LANvVj25Ey0nLKuGZGjIcDFBEZGMxmM0FBQW2O24+ZzWan3zMjI6NbMXX3+s4IbohgDFB7YCN709PbbRNhsM3M+GpPLudGVLs8Jjt39L83U//V/4HM1f1XEjbQRSbBztW2J2HNZsSF8jRZbD+i4hwiIu50uqmMXZnmOHXqVMcen86wWCxkZGR0+XqnjB0B2//AoMrDTJ84GnyHtGkyaHglT6d9Q5bZwpSp0/AyuXY1hVv73wup/+q/+t/1/tuvPxOnkrDa2lpuuukmfvaznzF37lyng5JeKKq5OEd+mqMyVVJsMAAHjldRfqKBoMHenotPRGSACA4ObvdpV3l5OUC7T8nOxGQydeuXqO5e3ylBkRAci8Gcg+loOow5p02T8SOCCPD1oqqukYPFJ5gc6fz3oivc0v9eTP1X/9V/1/XfqVtJfn5+ZGZmDugfSL8zbAoYveBEsa1ABxAW4OtYC5aWo6dhIiLuEB8fT2ZmZpvj9mPjxrWtHNhv2EvVd1Ccw2Q0kNh8g1Cl6kWkP3D6eX5iYiK7du1yRSziCd5+MGyy7etT9gsDtF+YiIibLFq0iKysLHbu3Ok41tjYyLvvvktCQoLTRTn6lJgW+4V1IDH25BYqIiJ9ndNJ2L333svrr7/O2rVrqa523+JYcSHHfmFtk7Dt2aWeiEhEpM/68ssvWb9+PZ9//jkABw8eZP369axfv56amhoA7r//fiZNmkR+fr7juquvvppx48Zxzz338N577/Htt9/y85//nMOHD/PrX//aI31xm+iZtte8bdDU1G4T+7ikGRoi0h84XZjj2muvpaGhgfvuu4/77rsPPz+/VouFDQYDqampPRqkuFhUEqS+0OpJ2IzmwW5nbjkNlia8XbwIWkSkv3jooYdaJVf2BAxgw4YNREdH09TUhMViwWq1Otr5+Pjw4osvsnz5ch5++GFqamqYOHEizzzzDCkpKW7vh1sNnwpeg6C2HEoOQMT4Nk2mxwRjMEB2yQmKq+oID/D1QKAiIj3D6STswgsv7PJGlNJL2Z+EHd1puwNpNDImIoAhfl5U1Day92gF06KDPRqiiEhf8dlnn52xzbJly1i2bFmb4+Hh4Tz66KOuCKt3M3nbbghmf2NbF9ZOEhY0yJtxQwPILKwiLbuMCyYP90CgIiI9w+kkrL1BQ/q4iAm2O5B1FVByECLiMRoNJMeF8Pn+IlKzy5SEiYiIa0XPtCVheVsh6fp2myTHhZBZWEVqjpIwEenbNMdMwOQFIxJsX7e7Lkzz70VExMViTl8hEU4W59iRbXZDQCIirtPlzZozMzM5dOgQdXV1bc59//vf705M4glRSZC72bYuLOGHACTHhQKqRCUiIm5gL1NftA9qzDAouE0T+83BnXlm6hub8PHSvWQR6ZucTsJqamq444472Lx5MwaDwbGouOU6MSVhfVA7FRITYoIwGQ0cLa+lwFxDZPAgDwUnIiL9XkAEhIyEsiOQvx3GLmrTZHS4P8GDvTGfaGDv0QoSYoLdHaWISI9w+hbSk08+SX5+Pq+88gpWq5UnnniCF154gfPPP5+4uDjefvttV8QprhbVnIQdywBLAwCDfbyYHDkE0JREERFxg5hZttfcbe2eNhgMJMVqH0sR6fucTsI2bNjAT3/6UxITEwEYMWIEc+bM4fHHH2fy5Mm89tprPR6kuEHoaPALgsZaOL7HcThJm2OKiIi7OPYL63hdWFJsMACp2i9MRPowp5Ow/Px8Ro8ejclkwmAwODaeBLj00kvZsGFDjwYobmIwQKQtsW61X9hIbdosIiJuYi/OkZfa4abNSXH24hxKwkSk73I6CQsMDOTEiRMAhIWFkZ2d7TjX2NjoOCd9UDvrwuyLoPceraS6rtETUYmIyEAxdDJ4+0NdORTvb7dJQnQwRgMUlNdytLym3TYiIr2d00nY+PHjOXLkCACzZs3i6aefZvv27ezatYuVK1cyYcKEno5R3MW+Lix/h+PQiKBBRAUPwtJkZWeu2TNxiYjIwGDyOjkW5W5pt4m/rxcTR9jWK6epVL2I9FFOJ2FXXXUV1dXVAPz85z+npqaG66+/nmuvvZaCggJ+97vf9XiQ4ib2J2HH90D9ySea9qkfWgQtIiIuZ18X1kFxDjg5S0Pjkoj0VU6XqL/kkkscX8fExPDRRx85ytUnJiYSHBzck/GJOw2JhIBhUFVoq5IYa6tSlRwbzHs7C1QhUUREXM+xLux0xTlCeGlTNmkqziEifVS3dzkcPHgw5557Luecc44SsL7OYGh3XdiMkc2bNueU0dRk9URkIiIyUNg3bS7OhBPtF4WyPwnbXVBObYPFXZGJiPQYbTUvrTnWhZ1MwiYMD2Swj4nK2kYOHK/yUGAiIjIg+IdB6Bjb1/mp7TaJDhlEeIAvDRYr3+WXuzE4EZGe0anpiBMmTMBgMHTqDQ0GA3v27DlzQ+md2nkS5mUyMj0mmG8PlZCaXcb44YEeCk5ERAaEmBQoPQS5W2Hc+W1OGwwGkuOC+Wh3IanZZY4ZGyIifUWnkrC77rqr00mY9HH2vcJKDkKNGQYFA7apH98eKmF7dik/nhXrsfBERGQAiJ4JO1d3WCERbOvCPtpdqHVhItIndSoJu/vuu10dh/QW/mEQHAfmbDiaDqPPBk7Ov09TcQ4REXE1e3GO/FRosoDR1KbJyQqJZqxWq24Wi0ifojVh0pb9aViLdWGJsSEYDHCk5ARFlXUeCkxERAaEoZPAJwDqq+D43nabTIkKwttkoLiqjtxSbdosIn2L0yXq165de8Y23//+97sQivQaUUmwZ22rdWFBg7yJHxrI/sJK0nLKuHDycM/FJyIi/ZvRBFHJcPhLW6n64VPaNPHzNjE5Moj0XDNpOWXEhg32QKAiIl3jdBLW0WbMLacBKAnr4+zFOfJ3tDqcFBdiS8KylYSJiIiLxaTYkrDcbTDjlnabJMWGkJ5rJjW7jO8nRrk5QBGRrnM6CduwYUObY2VlZWzYsIEPP/yQv//97z0SmHhQ5HTAABV5UHUcAoYCMCMuhNVbc7Rps4iIuF70mTdtTo4L4flvDqs4h4j0OU4nYVFRbe80RUVFMWXKFBobG3nppZdYtmxZjwQnHuIbCOHxULzfti5s/EXAyUXQGXnl1DVa8PVqu1BaRESkR0TPsL2WHITqElvhqFMkxQUDsPdoBdV1jfj7Ov1rjYiIR/RoYY45c+bw2Wef9eRbiqdEtd0vLC5sMOEBPtRbmrQ5poiIuNbgUAgbZ/s6b1u7TUYEDSIyyI8mK+zMM7svNhGRburRJCw/Px+jUQUX+wXHurCTSZjBYCAp1l4SWFM/RETExWLOPCUxSVuoiEgf5PRz+23b2t6Nqq+vZ//+/Tz99NPMmTOnRwITD2v5JMxqhebCKzNGhvDxnkK2HynjtgUejE9ERPq/mBRIfxVyT5OExYbw/q6jpOWY3ReXiEg3OZ2EXX/99W02RLRarQDMnTuX3//+9z0TmXjWsClg9IITJWDOgZA4oMWmzTll2hxTRERcy16cIz8NLI1gavtri8YlEemLnE7CXnrppTbHfH19iYqKIjw8vEeCkl7A2w+GTYajO21Pw5qTsMmRQfiYjBRX1ZNdcoKR4f4eDlRERPqtiAngOwTqKuD4HhgxrU2TiSOG4OtlxHyigaziasZEBHggUBER5zidhKWkpLgiDumNIpNsSVh+Gky+ArBtjjk1OojU7DJSs8uUhImIiOsYjbZNm7M+t60LaycJ8/EykhAdzNYjpaRmlykJE5E+wekqGocPH2br1vbnZm/dupUjR450NybpLRzrwlpv2myf+qH9wkRExOXsxTlOsy4ssblUvYpziEhf4XQStmzZsnY3bAb4/PPPtUdYf2KvkFiQDk1NjsPJqkQlIiLuEn3mJCw59uS6MBGRvsDpJCwjI4OZM2e2e27mzJl899133Q5KeomICeA1COoroeSA47C9TH3m8UrKaxo8FZ2IiAwE0cm217LDUFXUbhN7mfrMwiqNSyLSJzidhFVWVjJ48OB2z/n5+VFerk18+w2TF4xIsH3dYr+wiEBfRoYNxmqFHbrrKCIirjQoxHZTEDrctDk8wJe4MNvvJum5ZjcFJiLSdU4nYcOGDWPXrl3tntu1axcRERHdDkp6kZb7hbVgv+uoTZtFRMTloptn4Jxm02b7lESNSyLSFzidhC1atIhVq1axefPmVse3bNnCM888w/nnn99jwUkvYF8Xlt86CZsRFwposBMRETdwFOdo/0kYQGLzzUHN0BCRvsDpEvV33XUXX3/9NTfffDMjR45k+PDhHDt2jCNHjjB27FjuvvtuV8QpnmJ/EnYsAxrrwcsHOFmcIz3XTKOlCS+T0/m8iIhI5zg2bU4FSwOYvNs0sT8J25FjxtJkxWTUps0i0ns5/ZtzYGAgr7/+OkuXLiUoKIiCggKCgoK4++67+c9//kNAgPbn6FdCR4NfEFjqbBtlNhs3NIBAPy9O1FvYd6zSgwGKiEi/Fx5vG4saa6Cw/QJg44cH4u9joqqukQPHNS6JSO/m9JMwAH9/f+666y7uuuuuno7HKdXV1fzjH/9g3bp1lJeXM3r0aG677TYWL1582uveeust7rvvvnbPff3111rX1pLBAJGJkPWFbV1Y5HQAjEYDSbEhfJlZRGp2GVOigjwapoiI9GNGI0TNgEMbbFMSIxPbNDEZDUyPDeabgyWkZpcxYfgQDwQqItI5XUrCwFYlMT09nbKyMhYuXEhQkPt/Cb/77rvJyMjgV7/6FSNHjuT999/nl7/8JU1NTVx66aVnvP6RRx5h9OjRrY4FBwe7KNo+LDLJloTlp8GMWxyHZ8TZkrDt2WXcOHekx8ITEZEBIGaWLQnL2wqzbmu3SVJsCN8cLCEt28xPZsW5OUARkc7rUhK2cuVKnnnmGWprazEYDPz3v/8lKCiIG2+8kbPOOovbbmv/H8ee9OWXX/LNN9/wt7/9je9973sAzJ49m4KCAv7yl79wySWXYDKZTvse48aNY+rUqS6Ptc9zVEjc0eqwNm0WERG3iWmukHiaTZvtlXu1abOI9HZOrwl79dVXWblyJVdffTVPP/00VqvVce6cc87hiy++6Mn4OvTJJ58wePBgLrroolbHr7zySo4fP87OnTvdEseAYK+QeHwv1J9wHE6ICcZkNJBvruFoeY2HghMRkQEhagZgAHM2VB1vt0lSjC0JO1xcTWl1vRuDExFxjtNPwl599VVuuukmfvvb32KxWFqdi4uLIzs7u8eCO50DBw4wZswYvLxad2H8+PGO80lJSad9jyVLllBaWkpgYCApKSn87Gc/Iz4+vkvxnPq9cPa6rl7vFv7DMAYMw1BViKVgB8TMBsDPy8CE4YHsLqhg2+ESFk8d4fRb94n+u5D6r/63fB1outv/gfp9G7D8hsDQibYiUblbYeL32jQJGuzN2KEBHDxeRVp2GYsmDfNAoCIiZ+Z0Epabm8v8+fPbPefv709FRUW3g+oMs9lMdHR0m+P2tWlms7nDa8PDw1myZAnTp08nICCAzMxMVq1axbXXXsvq1auZMGGC0/FkZGQ4fU1PXu9qY/xHE1xVSMG29zle4uc4Hju4kd3A+u0HiLIUdvn9e3v/XU39V/8Hsv7UfxWMcrHomc1J2JZ2kzCApNhgDh6vIjVHSZiI9F5OJ2GBgYEUFxe3ey4/P5+wsLBuB9VZBkPHe4Cc7tyCBQtYsGCB488zZ85k4cKFXHrppfzzn//kqaeecjqWqVOnnnENWnssFgsZGRldvt5dDBVnQ+EmogzHiZw+3XH8QuNR1h3cSW6NF9NbHO+svtJ/V1H/1X/1v+v9t1/fm6hglIvFpEDavyGv402bk+NCeGN7ntYri0iv5nQSNmfOHJ599lnOO+88fH19AVvC09jYyOrVq5k3b16PB9me4ODgdp92lZeXAzhdrTE6Oprk5OQuryUzmUzd+iWqu9e7XPQMAIwFO6BFnDNH2ZLuPUcrqbNYGezTtYKbvb7/Lqb+q//qf9/vvwpGuUHMLNtrwQ5orAcvnzZN7EWjduaZabA04W1yevm7iIjLOf0v089+9jMKCgpYvHgxy5Ytw2Aw8Morr3DNNdeQnZ3NnXfe6Yo424iPj+fQoUM0Nja2Op6ZmQnYBjJnWa1WjEb9Y90u+54spYegxuw4HBU8iBFBfliarOzMLfdMbCIivYAKRrlB2FgYFAKNtVDY/lPQ0eEBDPHzorahiX1HtWmziPROTj+2iIuLY/Xq1TzyyCOsXr0aq9XKO++8w6xZs/jrX/9KZGSkK+JsY9GiRbzxxht8/PHHXHLJJY7jb7/9NkOHDiUhIcGp98vNzSUtLY25c+f2dKj9g38YBMfZqlIV7IAx5zhOJcWF8MGuo6RmlzJnjPumo4qI9Ca9rWAU9M+iUcaoGRgOfkJTzhasw6e32yYxNpgvM4vZdqSESSMCnP6M3tx/d1D/1f+WrwONu4pGdWnu2NixY3nuueeor6+nrKyMoKAg/Pz8znxhD1q4cCFnnXUWf/zjH6mqqiI2NpYPPviAjRs3snz5cseUj/vvv5+1a9fyySefEBUVBcBNN93EjBkzmDBhAv7+/mRmZvLss89iMBi455573NqPPiUqqd0kbIYjCdP8exEZuHpbwSjon0WjhpuiiQLMGR9z2GdWu21GeNcC8NnOwyQM6vrY1Bv7707qv/o/kLm6/11bwNPMx8eHYcM8V3loxYoV/P3vf+fxxx/HbDYzevRoHnvssVZVqJqamrBYLK32M4uPj2fdunU8//zz1NXVERoayuzZs7nzzjsZNWqUJ7rSN0Qmwe63oSCt1WH7/PvU7DKamqwYjR0XRRER6c96U8Eo6KdFo4LKYf8LhFQfIKiDglDVgSX8Z/c2DleiolFdoP6r/+q/64tGdSoJW7t2rVMf/v3vf9+p9l3l7+/Pgw8+yIMPPthhm2XLlrFs2bJWx+6//35Xh9Y/RTVPo8nf0erwxBFDGORtoqK2kUNFVYwbFuiB4EREPKu3FYyCflo0KmYmGIwYyvMwVR+HIW33qEyKC8VogHxzLcXVDQwb0rXZOr2y/26k/qv/6r/r+t+pJOx3v/tdp9/QYDC4LQkTNxuRABigIg+qjkPAUAC8TUYSYoLYnFVKanaZkjARGZDi4+N5//33aWxsbLUuTAWjephvIAydBIXfQd5WmHR5myYBvl6MHz6EvUcrSMsu4+KpbRM1ERFP6lQStmHDBlfHIX2BbyBEjIeifZCfBuNPVgCbERfK5qxStmeX8cOUWA8GKSLiGSoY5UYxKbYkLLf9JAxsmzbvPVpBWo6SMBHpfTqVhNkLWogQmWRLwgpaJ2H2dWHaHFNEBioVjHKj6BTY/vwZN21+dUuOikaJSK/U5cIcVVVVpKenYzabCQkJISEhgYAA58vASh8TlQQ7X7M9CWshKdaWhGUVV1NSVUdYgK8nohMR8SgVjHKTmBTba0E6NNaBV9sxxz4ufZdfQV2jBV+vgbu2RUR6ny4lYc899xxPPPEEtbW1WK1WDAYDfn5+/OxnP+Pmm2/u6RilN4lsLs5RkAZWKzRX+woa7M24oQEcOF5FWo6Z8yd5rmqmiIinqGCUm4SOhsFhcKIEju6yFes4RVzYYML8fSiprue7/ArHjA0Rkd7A6dW+a9euZfny5cycOZPHHnuMV199lccee4yUlBT+8pe/OF1JUfqY4VPA6G0b+Mw5rU7NGGkb4LZnl3oiMhERGSgMBohuTrzytnbQxECSpsqLSC/ldBL24osv8r3vfY9Vq1Zx8cUXk5yczMUXX8zTTz/N4sWL+fe//+2KOKW38PKFYZNtXxe0PyVRg52IiLicPQnLbT8JgxbjUo7GJRHpXZxOwrKysrjsssvaPXfZZZdx6NChbgclvZxjv7D2N23emVdOXaPF3VGJiMhAEjPL9nqG4hwAqdllrdbgiYh4mtNJmJ+fn2PjyVOVl5fj59e1DRGlD3GsC2u9afOocH9C/X2ob2xid0GFBwITEZEBIyoJDCaoyIfy/HabTIsOwsto4HhlHfnmGjcHKCLSMaeTsOTkZJ544gkKCwtbHS8qKmLlypXMmDGjx4KTXsr+JKwgHZqaHIcNBoNj6kfqEU39EBERF/LxPzk9voN1YX7eJiZHDgFQqXoR6VWcTsJ++ctfUlxczAUXXMCSJUv4/e9/z5IlSzj//PMpLi7ml7/8pSvilN4kfDx4D4b6Sig50OqUvTiHBjsREXE5e6n63I6nJCY23xzckWN2Q0AiIp3jdBI2btw4/vvf/3LeeeeRkZHBW2+9RUZGBueddx5r1qxh7NixrohTehOTF4xIsH3dwbqw7Zp/LyIirhZtT8K2dNik5bowEZHeokv7hI0aNYrHHnusp2ORviQyCXI22SokTv+R4/DUqCC8TQaKq+rILa0hNmywB4MUEZF+zb4/2NGd0FAL3m3XpdvL1O85WsGJ+kYG+3TpVx8RkR7l9JMwEaDDCol+3iamRAUB2i9MRERcLGQU+EdAU4MtEWtHZJAfw4f4YWmysiuv/cJiIiLu1qXbQXv27OG9996joKCAurq6VucMBgNPPfVUjwQnvVhkou31WAY01oOXj+PUjLgQduSYSc0u48qkaA8FKCIi/Z7BYJuSuP8DW3GO2FntNDGQFBfMhxnHSM0uY/boMA8EKiLSmtNJ2Nq1a7nvvvswGo2Ehobi7e3d6rzBYOix4KQXCx0NfsFQa4bjeyByuuNUclwIz2w8rPn3IiLiejEzbUnYGTZt/jDjGDu0abOI9BJOJ2FPPfUUCxcu5NFHHyUoKMgVMUlfYDDYnoZlfW5bF9YiCbPPv99fWElFbQND/Lw7eBMREZFushfnyNsGVqttfDqFvThHWo4Zq9WqG8Yi4nFOrwk7fvw4N9xwgxIw6XBd2NBAP2JDB2O1QrpKAouIiCtFJoLRCyqPQnluu00mRwbh42WktLqeIyUn3BygiEhbTidhEydObLNRswxQkfZNm3e0OTWjRal6ERERl/EZDMOm2L7uYEqij5eRac1FozRVXkR6A6eTsN/+9resWrWKffv2uSIe6UvsT8KO74X61ncW7VMS0zTYiYiIq8W0mJLYAce4pHVhItILOL0mbPr06VxwwQVcccUVREREtJmWaDAYePfdd3ssQOnFhkRCwHCoOgbHdkHsbMepGSNtg92OnDIaLU14mbQbgoiIuEjMLNi66ozFOUA3B0XkpAZLE7mlJ8gqquZwcTVZxVXklZ5gZngj06e79rOdTsJWrVrF008/TWhoKJGRkW2qI8oAE5UE+z+0rQtrkYSNGxpIoK8XlXWN7C+sZHKk1hCKiIiLRDdv2nxsFzTUgPegNk2S4oIBW9GoytoGAlU0SmRAsFqtFFXVkVVU3ZxsVTmSrpzSEzQ2WdtcU3fCl7tcHJfTSdhLL73EVVddxZ/+9CdMJpMrYpK+JLI5CStoXZzDZDQwPTaYjQeKSc0uUxImIiKuExwLAcOgqhAK0iFuTpsmQwP9iAkdRG5pDem5ZuaPi3B/nCLiMifqG22JVnE1h4tsT7UON39dWdfY4XWDvE2MCvdndIQ/o8P9GRk2mKENx1wer9NJWHV1Nd/73veUgIlNVPOmzadUSASYERfqSMJumDPSvXGJiMjAYTDYnobte9+2aXM7SRjYpiTmltaQlq0kTKQvarQ0kW+ucSRbWUW2RCurqJpjFbUdXmc0QHTI4OZEK4BREf6MCfdnVIQ/w4f4tdq2wmKxkJ5+3OV9cToJS0pK4tChQ8yZ0/4/cDLA2Csklh6CGjMMCnacsu/Lsv2I5t+LiIiLxaTYkrDTrAtLjgvhnfQCUlWcQ6TXslqtlFbXO55oHSquan6yVU1OyQnqLU0dXhvm7+N4qjUqPMDxdCs2bDC+Xr3rAZLTSdgDDzzAz372M4YPH86CBQvw8fFxRVzSVwwOhZCRUHbEVqp+zDmOU9NjgzEaIN9cw7HyWoYH+XksTBER6efsmzbnbu1w02Z7cY4dOWU0NVkxGrVps4in1DZYbNMFm59oZTU/0coqqqKituPpg75exhaJ1sknW6PD/Qke3HfyEqeTsKuuuorGxkbuvvtuDAYDfn6tf7E2GAykpqb2WIDSB0QmNSdhaa2SsABfLyYMH8KeoxWkZpexeNoIz8UoIiL9W+R0MHpD9XEwZ9tuEJ5iwvBABnmbqKxt5GBRFfHDAt0epshA0tRkJd9c40i0bBUIbclWQXkN1rY1MQDbPZTIoEGOJ1mjIwIcSVdk0KB+cQPF6STswgsvbDVvUoSoJNj9VvvrwkaGKAkTERHX8x4EI6ZBfirkbms3CfMyGZkeE8ymrBJSs8uUhIn0EPOJ+lZPsuzrtI6UVFPX2PH0waBB3o51WvaEa1SEPyPD/PHz7l3TB3uaU0mYxWLh9ttvJzQ0tM3+YDKA2deFFexocyo5LoSXNmWTml3q5qBERGTAiU6xJWF5W2HaNe02SYqzJWFp2WX8KCXWzQGK9F11jRZySk5wyF550F6JsLia0ur6Dq/zMRmJCxvcZp3W6IgAQgZ7D9iHO04lYVarlcWLF/PUU0+xcOFCV8Ukfc2IBDAYoSIfKgshcJjjlL04x+6CCmrqLQzy6d93NURExINiZsKWp85YnANQcQ6RDhRX1bGrsI7vNudwpMVGxnllJ2hnSy2HEUF+bdZpjQkPICpkEKZ+MH2wpzmVhHl5eREeHo61owmcMjD5BkD4eCjaa1sXNv5ix6mo4EEMG+JLYUUdu/LMzBod5sFARUSkX7MX5ziWAfXV4OPfpklijC0Jyyqqpqy6nhD/vrOQX8RVrFYrqdllPP1VFp/uLWxeq9X2RkWgr9fJRCsioFWBjME+Tq9yGtCc/m4tXryYtWvXcvbZZ7sgHOmzopJsSVh+6yTMYDAwIy6UDzKOsj27TEmYiIi4TlA0BI6AyqO2KfIj57VpEuLvw+gIf7KKqtmRW8a5E4a180YiA4OlycrHu4+xamMWO3LMjuORASYmRocyZmigbZ1Wc9IVHuAzYKcP9jSnk7AJEybw4YcfcsMNN3DBBRcQERHR5odxwQUX9FiA0kdEJkL6q7YnYadIigvhg4yjpGVr6oeIiLiQwWDbL2zPO7Ypie0kYWArVZ9VVE1atllJmAxINfUW/puay7NfHya75AQAPl5GrkqK4ua5cVTmH2T69OmYTFpG4ipOJ2H33nsvAIWFhWzd2nbOtcFgYO/evd2PTPqWqObiHPlpbfZnmdFi/r32ZREZuKxWK4UVdewvrCTzWCX7j1UwuLGa6dM9HZn0K9HNSVjetg6bJMeF8N/UPFJ1c1AGmKLKOl7edISXN2dTdqIBgODB3lw/O44b5owkItAXi8VCer6HAx0AnE7CXnrpJVfEIX3dsCm2/VlqStvszzIpcgh+3kbMJxrIKq5m7NAAz8UpIm5RVl1vS7YKK9l/7OTrqRtw+hjhgaubdLdVek5M5zdt3plnptHShJfJ6M4IRdzuUFEVz248zJtpedQ3l4yPCR3ErfNGc82MaK3n8gCnv+MpKSmuiEP6Oi9fGD7FNgc/P61VEuZtMpIQHcyWw6WkZpcqCRPpR6rqGjngSLaqbK+FlRRV1rXb3mQ0MCrcn/HDAhk31J8oQ5l+AZaeNSIBTD5wohjKDkPo6DZNxg0NINDXi8q6RvYdq2RKlLbdkf7HarWy7UgZq5qLbdglxARz+4LRXDh5uKoWelCX097KykrS09MpKytj4cKF2jdMbPuFFeywrQubcmWrU8lxIc1JWBnXztS+LCJ9TW2DhUNFVY5k60BzspVXVtPhNTGhgxg/LJD4YYGMH257HR3hj6+X7amXxWIhPT3dTT2QAcPL15aI5W2zPQ1rJwkzGg0kxoXwVWYRO3LKlIRJv2JpsvLR7mOs+iqL9Fyz4/iiicO4feFoZsSFqLhGL9ClJGzlypU888wz1NbWYjAY+O9//0tQUBA33ngjZ511FrfddltPxyl9QVQSbH8O8tvftBlgu+bfi/RqjZYmjpScaD2NsLCSI8XVHe4PM2yIry3RGhZI/HDb69ihAfj7anqLeEh0yskkLOGH7TZJig3mq8wiUrPLuH7OSPfGJ+ICJ+obWbM9j+e+PkxOactiG9HcOn8UYyI0E6k3cXqEfPXVV1m5ciU//vGPmT9/Prfffrvj3DnnnMPHH3+sJGygimwuznE0HZosYDy5xsM+/z6ryLareqj2ZRHxqKYmK/nmGkeSlXmskv2FVRw6XkW9panda4IGeTN+eOtkK35YAMGD9f+z9DIxM2EzkKdNm6X/K6qs46XmYhvm5mIbIYO9uX7OSG6YE0d4gK+HI5T2dCkJu+mmm/jtb3+LxWJpdS4uLo7s7OweC076mIjx4O0P9VVQfACGTnCcCvH3YUyEP4eKqknLLmPRJJUEFnEHq9VKUWVzRcLCquZkq5IDhZVU11vavWawj4lxwwIZPyzAMZVw/LBAIgJ9NYVF+gb7ps2Fu6GuCnzbPgGYHhOMwQC5pTUcr6xlaKCfm4MU6Z6Dx6t47uss3kzLdxTbiAsbzK3zRnF1cgyDfFTwqDdzOgnLzc1l/vz57Z7z9/enoqKi20FJH2U02ebh53xrWxfWIgkDmBEXyqGialJzlISJuEL5iQb2t3qyZZtOaL8zeiofk5HREf6O9VrjmxOuqOBB2kpC+ragKBgSDRV5tvFo1II2TQL9vBk/LJB9xypJyzZz0ZThHghUxDlWq5Wth0t5ZmMWn+497jg+vbnYxgUqttFnOJ2EBQYGUlxc3O65/Px8wsLCuh2U9GFRSbYkLD8Npv+41ankuBBe355L6hFN/RDpjhP1jRworGqTbBVWtF+R0GiAkWH+xLeYRjh+eABxYf54qzKh9FcxM2F3nm1dWDtJGEBibAj7jlWyI6dMSZj0ao2WJj7aXciqrw6xM68csO2+cP7EYdy2YDTJKrbR5zidhM2ZM4dnn32W8847D19f2xxTg8FAY2Mjq1evZt689nenlwEiMtH2WpDW5lTyyJP7stQ3NuHjpV/+RE6nrtHC4eLqFvts2aoT2hdctycqeNDJJ1vDbdMJx0QE4OetaSkywESnwO63z7hp8+qtOdq0WXqt6rpG1mzP5blvDpNbaqtG6+tl5KrkaG6dN4rRKrbRZzmdhP3sZz/j6quvZvHixSxatAiDwcArr7zC3r17KSgo4B//+IcLwpQ+I6q5OMexDGisB6+TC/ZHh/sTMtibshMN7C4oJ7G5WIfIQGdpspJdWtVmr63DxdVYOihJGB7g60iy7IUyxg0NINDP283Ri/RSndq0ORiAXfnlujkovcrxylr+/e0RXtmcQ3nNyWIbN8wZyfUqttEvOJ2ExcXFsXr1ah555BFWr16N1WrlnXfeYdasWfz1r38lMjLSFXFKXxEyCgaFQE0ZHN998skYtiemyXEhfLr3OKnZZUrCZMAqq67nm0PFbMwsYuvBYgre/oS6xvYrEgb6eZ1SjdBWkTBMA7DI6Q2fBiZfqCmFkkMQPrZNk1G6OSi9zMHjlTzz1WHe3pHvqFQ7Mmwwt84fzVVJ0Sq20Y90aROXsWPH8txzz1FfX09ZWRlBQUH4+amqkGC70xiZCIc+s60La5GEASQ1J2FpKgksA0htg4XtR8r4+mAxXx8sYndBBdZTHnD5eRsZN7T1NMLxwwMZPsRP8/z7kOrqav7xj3+wbt06ysvLGT16NLfddhuLFy8+47UlJSUsX76czz//nNraWiZMmMDPf/5z5syZ44bI+yEvH9sYlLvZVqq+nSTMYDCQFBvChn3HScsxKwkTj7BarWw5XMozX2WxYd/JYhtJscHctmAM508apmIb/ZDTSdh9993HnXfeSUxMDD4+PgwbdrLKXX5+Pk888QSPPPJIjwYpfUxkki0JK0gD/qfVqRlxoQBsP1KG9dTfQkX6iaYmK3uOVvD1wWK+OVjM1sOlbZ50TRgeyNwxoYRby7lw9lRGhgdqkO0H7r77bjIyMvjVr37FyJEjef/99/nlL39JU1MTl156aYfX1dfXc9NNN1FRUcEDDzxAWFgYr776KrfeeisvvPACKSkpbuxFPxIz05aE5W5tUyzKLimuOQnLLuN/5o1yc4AykDVamlj33TGe2ZjFrhbFNi6YZC+2EerhCMWVnE7C3n77bX70ox8RExPT5lxZWRlr1651WxKmO469lH1dWP6ONqemRQfhbTJwvLKOvLIaIoM0pUr6h3xzDV8fKOLrgyV8e7CYkur6VueHDfFl3tgI5o0L46yx4QwN9MNisZCens7IMH8lYP3Al19+yTfffMPf/vY3vve97wEwe/ZsCgoK+Mtf/sIll1yCydT+VKI1a9aQmZnJf/7zHxITbTMIZs2axeWXX87y5ctZs2aN2/rRr9j3CztDcQ5AMzTEbarrGnljey7PfX2YvLKTxTaumRHN/8wbzahwfw9HKO7QpemIHSkvL8fHx+fMDXuI7jj2UpHNSVjRXqivBp+T/5j4eZuYHBlEeq6Z1OwyIqepJLD0TRW1DWw6VMLXB2xPu7KKq1ud9/cxMXt0GPPGhTNvbDhjhwZoWmE/98knnzB48GAuuuiiVsevvPJKfvWrX7Fz506SkpLavfbTTz9l1KhRjgQMwMvLi8suu4zHHnuMwsLCVjNPpJPsxTmO74HaCvAb0qbJtOggTEYDR8trKTDXEBk8yM1BykBxvKKWF789wiubs6mobQQg1N+HG+bEcf3sOK31HWA6lYRt27aNLVu2OP68Zs0avvrqq1Zt6urq2LBhA2PGjOnZCDugO4692JAREDgCKo/C0V0Q1/rpYnJciCMJu1RJmPQR9Y1NpOea+fpAERsPFrMz10zLwoUmo4GE6CDmjYtg/rhwpscEaw+uAebAgQOMGTMGL6/WQ+v48eMd5ztKwg4cOEBycnKb4y2vVRLWBYHDISgWynMgPxXGnNOmyWAfLyaNGEJGfrnt5qCSMOlhBworeWZjFmt3FDiKbYwK9+fW+aO4KilaW4gMUJ1KwrZs2cITTzwB2BaxdpSkREZG8r//+789F91p6I5jLxeZBPs/sK0LOyUJmxEXwnNfH2a79mWRXsxqtXLgeBVfHyjm64PFbM4q4US9pVWb0RH+zBtre9I1e0wYQ1QefkAzm81ER0e3OR4UFOQ4f7pr7e2cvfZ0LBbLmRud5rquXt+bGKJnYCzPoSlnC9aR7W/aPD0mqDkJK+WSKcP6Vf+7Qv3vfv+tViubD5fy7MYjfJFZ5DieHBfMT+eN4rwJQzE2T0Pvbd9n/fy71//OXtepJOzWW2/lJz/5CVarlblz5/Lcc88xadKkVm18fHzw93ffHNbedsdRA11rhhHTMe7/gKa8VKyn9C0h2jYdZP+xCsqr64D+1//O6q8//87qbf0/XlHLN4dK+OZQCd8eLKGwsq7V+VB/H84aE2b7b2xYmzvmzvajt/Xf3dw10LnT6aacnmk6aneu7UhGRkaXruup63uDCEMksUDl3g0cHHJ+u23CrLZ1OV/vLSA96uT/9/2h/92h/jvff0uTlU15tbybWc2hMtuUQwMwK8qXy8b7Mz7MB+qPsmvX0R6Otufp5+/a/ncqCfPz83OUoN+wYQMRERFuXfvVnt52x1EDXWuBNUOIB+qPbGZ3enqb80MHmzh+wsLar9NJGObb7/rvLPXfM/2vbWxid1EDuwrr2FVYT05FY6vzPkaYGOHDtGG+JAzzIS7IC6PBChRz/Egxx9t/W6fp598/+h8cHNzu+FFebqt61t640xPXns7UqVM7nJp/OhaLhYyMjC5f36sMtcJ3TzCkIpPpCdPA0HaacHjcCf6x5SuOlDcyYfJUvI30n/53Qb/6+XdBV/pfVdfImtQ8XvjmCPnmWsC29cjVSdHcfFYcI8P6TrEN/fy713/79WfidGGOqKgop4Nxld50x1ED3Slq4mDLvfhV5zN9/EgYFNzq9JzMnbyz8yhlphDgRP/rfyf1259/J7m7/5YmKxn55bYKhoeKScsx02A5ubDLYIDJI4Ywb2wYc8eGMyM2GF8XztXXz989A527xMfH8/7779PY2NhqlkZmZiYA48aNO+219nYtdeba0zGZTN36u9Xd63uFyATwGoSh1oyp7DBExLdpEhsWwNBAX45X1rH7aBXJsbakt1/0vxvU/zP3v7C52MarLYpthPn7cOPckVw3O45Qf88+tOgO/fxd23+nk7CGhgaeeeYZ3n//fQoKCqiraz1dx2AwsGfPnh4LsCO97Y6jBrpTBIRDyCgoO4ypcCeMObfV6RkjQ3ln51F25JZzdrh3/+u/k9R/1/TfarWSXXKCjQeL+fpAEZsOlTgGSbvokEHMHxfOvLERzBkT5pEBUz///tH/RYsW8cYbb/Dxxx9zySWXOI6//fbbDB06lISEhNNe+9BDD7Fz505Hu8bGRt59910SEhK0Trk7TN62TZtzvrVt2txOEmbftHn97mOk5ZQ5kjCRjmQWVrLqqyzeSc933MwbHe7PrfNHc2VSlIptyBk5nYQ99thjvPjiiyxYsIBFixZ5bFpib7zjKKeISoKyw5Cf1iYJs29AmJ5rxjI93BPRST9VWl3Pt4eK+fpAMRsPFJNvrml1foifF3PHhDtKx8eFDVbpeOkRCxcu5KyzzuKPf/wjVVVVxMbG8sEHH7Bx40aWL1/uSDTvv/9+1q5dyyeffOKYXXL11Vfz2muvcc899/CrX/2KsLAwXnvtNQ4fPswLL7zgyW71DzEzbUlY7lZIvK7dJslxtiQsNbuMn84b6d74pE+wWq1sOlTCqo1ZfLH/ZLGNlJGh/HTB6FbFNkTOxOkkbN26ddx1110sXbrUFfF0mu449gGRSfDdm1DQdtPm8cMDCfD1oqqukdzyRtqWSRHpnNoGC9uPlPH1wWK+PljE7oIKrC1Kx3ubbHe4548LZ964CKZGBWljZHGZFStW8Pe//53HH38cs9nM6NGjeeyxx1i8eLGjTVNTExaLBWuLv6g+Pj68+OKLLF++nIcffpiamhomTpzIM888o70re4J90+bcrR02SYoLBmBHTlmrn41Ig6WJDzOO8szGLL7LrwDAaICLpgznp/NHkxgb4uEIpS9yOgkrLy9nxowZrojFKbrj2AdENVenzE9rc8pkNJAYG8zGA8XsK2lwc2DSlzU1WdlztIKvD9o2Sd56uJS6xqZWbSYMD+SssbanXbNGhTLYp0f3pRfpkL+/Pw8++CAPPvhgh22WLVvGsmXL2hwPDw/n0UcfdWV4A5d90+aifVBbDn5tpxtOjgzCx2SkuKqenNKaNudl4LEV28huLrZh+zvh523k2hkx3DJvFHF9qNiG9D5O/2Yyc+ZM9u3bx+zZs10Rj1N0x7GXG5Fgq0JVWQCVx2ybZraQFBvCxgPF7C2q91CA0lfkm2tsmyQfKObbQyWUVrf+OzNsiC/zxkYwb1wYZ40NZ2ign4ciFZFeKWAohIyEsiOQtx3GntemiZ+3iSlRQ0jLMbMj18xId8covcax8lpe3lXJhve+oLJ5HXF4gA83zrEV2wjpw8U2pPdwOgl78MEHufPOO4mMjOTss8/2aKl63XHs5Xz8IWICHN9jexo24ZJWp2eMtD2+/zq3lmue3swNc0Zy8dTh+HppMetAV17TwOasEsdGyYeLq1ud9/cxMXt0mGNd19ihAVrXJSKnF53SnIRtazcJA9vNwbQcM2nZZYyMc2944nk5JSd46suD/Dc172SxjQh/bps/mu8nqtiG9Cynk7DLL7+cxsZG7rnnHgwGg2P/MDuDwUBqamqPBSh9XGSSLQkraJuEzR4dxhWJkbybXmAb9HLS+b/3fbh2Zgw/nhVLdMhgDwUt7lbf2ERGTrntadfBYnbmmmlqsSTDZDSQEB3EvHERzB8XzvSYYLxNbff6ERHpUEwKZLxx2nVhyXEhPPv1YXbkmrkyTlPNBopDRVU8+fkh1qbnY2kefCaGe/OLi6ayaNJwFdsQl3A6Cbvwwgt1x1k6LyoR0l9pd12Yt8nIX6+exuLoBr47EcR/tuVxrKKWJ784xL++PMS5E4Zx/Zw45o8N1z+A/VBZdT3vpufz7vYy9r6zgRP1llbnR4f7O550zR4TxhA/bw9FKiL9QvRM22vedmhqAmPbGzlJcbYZGvuOVVLTMMid0YkH7D9WyROfH+T9XQWOgk4L4yO48+zReJVlM32iqh2K6zidhLU3tU+kQ5HNxTkK0sBqte2Ge4oQPxN3zx7L0nPH8ene47y8+QjfHCzh072FfLq3kLiwwVw3K45rZkQTPFjzsPuy+sYmvth/nDfT8vhs3/FWGyWH+vtw1thw5o8N56xx4UQF6xcgEelBw6aA92CoK4fi/TB0YtsmQ/yICh5EvrmGA6UNzPFAmOJ63+WXs+KzA3y0u9BxbNHEYdx97lgSYoKxWCykl2V7MEIZCFQyTFxr2BQw+UBNmW0ufuioDpt6mYxcNGU4F00ZzsHjVby6JZv/puaRXXKCP3+4l79+vJ/LEiK5fk4c06KD3dYF6R6r1cp3+RW8mZbHuzsLWhXVmBw5hKSwJn6wYBqTo4J1x1FEXMfkZbsxmP21bUpiO0kY2J6G5ZtreC/zBHMSq5gwQhs39xdpOWU88dlBPtt3HLDdF75kygjuOmcskyKHeDi6XqLqOIa0l4nKycRQFmX7/8boBUYTGEwnv+70seave+SYV7tPsPuqTiVhu3fvdupNJ0+e3KVgpB/y8rElYgVptv9Ok4S1NHZoAH+4dDK/uXA876QX8NKmbPYerWBNah5rUvNIiAnm+tlxfG/aCC2U7aWOV9Ty9o583kzLI7OwynE8ItCXKxKjuCopmrERg0lPT2dS5BAlYCLiejEptiQsbysk39huk3PGR/DezgLSjtVx0T+/Zu6YMG6cO5LzJgzFS2tR+6QtWSWs+OwgXx8sBmx7fF2WEMld54xl3LBAD0fXS9SUwTePw5Z/YWw4wXCAg54OqgPtJWZOJ3WnJI8tjhmM3gQEzgamu7QbnUrCrrrqqk6tA7NarRgMBvbu3dvtwKQfiUqyJWD5aTDlKqcuHezjxY9SYvnhzBjScsy8sjmbD3YdZWeumZ25Zh7+YA/XzojhJ7PiiA1TIQ9Pq22w8PGeQt5MzWPjgSJHcQ0fLyMXTBrGVcnRzB8b7vhFxmKxnObdRER6mH2/sNxtHTa5MimaiAAfnvhoF9sK6vj2UAnfHiohKngQP5kdyw9nxhKqEuW9ntVq5euDxazYcJCtR0oB8DIauDIpijvPHsvIcBVeAaCuEjb/C75dYZuqC1gjkzjuN5qI8FCM1iZoskBTo+3Vav+6+c9tjjWd/Npx/NRjLd6v3WNn2D+2qRFoBEudS74lRmDYsGw4r/0bNT2lU0nYI4884tIgpJ9zrAvb0eW3MBgMJMeFkBwXwgOLJ/LG9lxe3ZxDvrmGp7/KYtXGLBbGR3DDnDgWxg/FpKcqbmO1WknNLuPNtDze33XUsacKwIy4EK5KjuaSqSMIGqTCGiLiYfbiHMX7bXf+B4W022zumDAGzw0hIm48q7fn8Z+ttvHmL+v3849PD3DptEhunKup8b2R1Wrl8/3HeXzDQdJzzQD4mIxcMyOaJQvHEBOqG7YANNTC9udg42NwwvaEkKGT4NwHaRp7IXk7dxI+fTqYPDTbqKmpRRLXXhLoTGJoOSXhO/2xpiYLuZY4Jrm4i51Kwq644goXhyH9WpQ9CUu3/QU3du9/6PAAX+48eyy3LxjD5/uO8/LmbL7MLOKL/bb/okMG8ZNZcfxgRjRhAb7dj1/alVt6grfS8nlrh23dnl1U8CCuSoriyqRo3WkUkd7FPxxCR0NpFuSlwrhFp20eFTKIey+awD3njeP9XUf597dHyMgv5820PN5My2N6TDA3zo3jkqkjtMelhzU1Wfl4zzFWfHaQ3QUVAPh6GfnxrFhuXzCG4UF+Z3iHAcLSADtehi+XQ2WB7VjoaDjnAZh8pW1qX2+YpWI0AkYwuf8GrtVioT493eWfo8Ic4nrh8eDtDw3VUJzZ4WJoZ5mMBhZNGsaiScM4UlzNq1uyeWN7HnllNTy6fh9//yST700bwXVz4kiMCdbWCj2gqq6RDzOO8mZqHlsOlzqO+/uYuGTqCK5MimbWqFCt7xKR3is6xZaE5W45YxJm5+dt4urkaK5KiiI918xLm7J5f1cB6blm0l838/D7e/lRSiw/mR3LiCBVdnUnS5OVDzKOsvKzg+wvrARgsI+J62fHcev80UQE6mYsYLsJnvFf+OL/2QqlAQyJhoW/hek/9kiyM9ApCRPXM5ogcjpkf2NbF9ZDSVhLI8P9eWDxJH51wXje21nAy5uz2ZVXzls78nlrRz6TI4dww5w4LkuIYpCP7lY6w9Jk5dtDxbyZmsf63ceobWgCbFWlzhoTzlXJUVw4eTiDffTPiYj0ATEzYdd/bMU5nGQwGEiMDSExNoT7L5nI69tyeGVzDscqanni84M89eUhLpw8jBvmjGTWqFDd/HOhBksT76QX8OTnB8kqrgYg0NeLm84ayS1njSJE6/ZsrFbY+x58/mco2mc75h8B838NyTeBt54Qeop+axL3iEy0JWEFaZD4E5d9jJ+3iWtmxHDNjBh25pp5eXM27+4sYHdBBfe+mcGfP9jL1ckxXDc7ltERAS6Loz84eLyKN9PyWLsjn6PltY7joyP8uSopmisSo4jUXl4i0tfEzLK95qV2a4p8RKAvS88dx5KFY/hkTyH/3nSEzVmlfJhxjA8zjjF+WCA3zI3j+9Oj8PfVr1s9pb6xiTfT8njyi4PkltYAEDzYm/85axQ3zB2p9cd2Visc3ACf/R8cTbcd8wuCs+6BWUvAR8sFPE3/Koh72NeF5ae57SMTYoJJiAnmgUsmsiY1l1c255BTeoLnvznM898cZv64cK6bHaeywy2YT9Tz3s4C/puWz87mBc0AQYO8uTRhBFclRTNdUztFpC8bOgl8AqC+0vZkYFj3ttXxMhm5eOoILp46gn3HKnhpUzZvp+Wzv7CSB97+jmXr9nFNcgzXz4ljlNbJdlltg4XXt+Xyry8POW4Mhgf4cOv80Vw3O44AJbonZX8LG/4Pcr61/dnbH+bcCXOWwqBgj4YmJ+lvrLiHvUJi4XfQWG/bP8xNQvx9uG3BGG6dN5qvDhTx8qZsPtt/nI0Hitl4oJjIID9+PCuWa2fGDsi54w2WJr7YX8RbaXls2HuceottuqHJaOCc8RFclRTNuROHatG5iPQPRpPtxuDhr2ybNnczCWtpwvAh/L8rpnLvRRP4b2oeL286wpGSkzf/FsZHcOPcOM6OH6q1s510or6RVzfnsGpjFkWVtpLkw4b4cvuCMfwoJVZLDFrKT4PPHoZDG2x/NvlCyk9h3i9sRWmkV1ESJu4RMhIGhUJNqS0Rsz8ZcyOj0cDZ44dy9vih5Jae4LWtOby+LZeC8lr++nEm/9xwgIumjOCGOXHMiAvp1097rFYruwsqeDMtj3fTCyiprnecmzRiCFclR3P59EjCVV1SRPqj6BRbEpa3DWbc3ONvHzTIm/+ZN4qb547kqwNFvLQpm8/3H+fLzCK+zCwiNnQw18+O4wczYggarOlz7amsbeClTdk89/VhSpvHqKjgQSw5ewzXJEfj563ky+H4Xlvyte9925+NXpB4PSz4DQRFeTY26ZCSMHEPg8G2LuzQBtu6MA8kYS3FhA52lB1e991RXt6UTVqOmfd2FvDezgImDA/kutlxXJHYv+byH6+o5Z30At5My2PfsUrH8fAAX65IjOTKpGgmjhjiwQhFRNzAsWmz88U5nNHy5l92STWvbM7m9W255JSe4M8f7uVvn+znisQorp89kkmR+rcXoPxEA89/c5gXvjlMRfO+k3Fhg7nr7LF8PzEKHy8tH3AozYIvlsGuNwArYIBp18LZ99rKzkuv1n9+u5TeLyrJloTl74CZng7Gxs/bxBWJ0VyRGM13+eW8sjmbten57DtWyYNrbXP5r0qK4rrZcYwbFujpcLuktsHCJ3sKeTMtj68yi2iy2o77eBk5f9Iwrk6KZv64cK2LE5GBw75pc8kBOFEKg0Nd/pFxYbYqvr88fzzvpOfz4rdH2HesktVbc1m9NZeUkaHcMDeOCycPx3sA/ntcUlXHc18f5qVN2VTV2ZKvMRH+LD13LJdOi9QY1VJ5Pnz1F9jxim2TYYCJl9n2+ho6wbOxSacpCRP3sa8LK3BfcQ5nTIkKYtlV07jvkom8mZrHK5uzySqu5t+bsvn3pmxmjw7lhjkjOX/SsF4/QFqtVtJyyvhvaj7v7yqgsvluIkBSbDBXJUfzvamRmgYjIgPT4FAIGwslB21TEuMvdNtHD/Ix8cOUWK6dGcP27DJe/PYIH313jK1HStl6pJShgb78ZFYcP5oVw9DA/l8+/HhFLc9szOKVzTnUNNg2CZ4wPJC7zx3HRVOGY9LauZOqiuDrv8O2Z8FiWx/H2EVw7oO22UbSpygJE/exT0Es2gf11b22PGrQIG9umTeKm+aO5NtDJby8+Qif7Clkc1Ypm7NsA+SPUmL58axYhg3pXQNkXtkJ3k6z7Y12uHnfFLDNo78yKYorEqNUml9EBGyl6ksO2qYkujEJszMYDMwcGcrMkaEcK6/lta05vLYlh+OVdfz900ye+PwAF08ZwY1z40iK7X/rlAvMNTz95SFWb8ulvtFWEGpadBBLzxnLoonDVLikpRozfLsCNj8FDc1je9xZcO7vIW6OR0OTrlMSJu4TOBwCI6GyAI7uhLi5no7otIxGA/PGhTNvXDgF5hpWb81h9dZcjlfW8c8NB3ji84NcOHkY182OY87oMI8NkNV1jXyYcZQ30/LYnFXqOD7Yx8TFU0ZwVXIUs0eFaUATEWkpeiakv9qlTZt72vAgP355fjxLzxnLuu+O8tKmbFKzy3h3ZwHv7ixgcuQQbpwzksumR/b5ghQ5JSd46suD/Dc1jwaLbX58clwId587loXxEf0u2eyWuirY8i/49nGoLbcdi0y0JV9jzrWtt5c+S0mYuFdUEuwrsJVR7eVJWEuRwYP41QXjufvccXy0+xgvb8pm65GTm3KOHRrA9bPjuCIpiiF+rp/i19RkZVNWCW+m5rHuu2OOKRwGA8wdE8aVidFcNGV4vyoqIiLSo+zFOfLTurVpc0/y8TJy+fQoLp8e9f/bu/O4qOr1geOfGRZlURYRN1AEZRMCRRb3JSs118zs/kptMTPTFm3TujftmpbmrcSr5rXMylt2c8klNfelBA2XVFxwB1RQFBUXluH8/vjK4AQqCsww8Lxfr/Pi5TlnmOc7M87Dc853YV/qJb7ZdoKfd59m/+nLvLXwTyauPMCAlt48HdMIb3dHS4d7T46ey2LGhqMs2Z2K4ebg5Fa+tRjZuQmt/Cx3IbNCyr0BCXNhy1S4ek7tqx0End+FwB5SfFUS8heaMK/64WoK1Qo6Luxu7G319AyrT8+w+hw8e5nv4tSinEfSs3h/6X4+XnWQPs0bMKhVIwLrlv1MV0fPZbEwIYUlu1I5fXOxSgBfDyf6RXjRp3kDGrg6lPnzCiFEpVM7EOxrqEWb0xOhbqilIzIR0sCFyY+HMaZbED/+kcy3cSdJuXidLzYfY/aWYzwY6Mng1j608fOo0D0dDp29wvQNR1jx52njxFDt/WvzSucmtPQp/wlRrIohV92d3TQZLqeqfW6NodNYCOlXIS4UiLIjRZgwr4LJOVKtswi7VWDdmkzooxblXLwrlW+3nSQpPYv/xqt+/ZE+bjwd04huIfVKNaVu5rUclv15hoUJKexOzjTur1ndlp5h9ekX4UVzb1e5iiiEEPdCbwNeEXBsIyTHV7girICbkz0vdvBjSDtfNhxMZ962E2xJOs/aA+msPZCOb20nBsU0ol+EFzXM0BOjpPalXiJ2fRKr96cZ93UJqsOIzk0I93a1XGAVUX4+7FsIGyeqaecBajaADm9B+FNgU3HeV1F2pAgT5lUwe8/F42pa4Goulo2nDNSobsegVj4MjGlE3LELfBd3ktX7z7LjxEV2nLjIP50TeTKyIX+Lbljiu1S5hnw2Hz7Hwp0prE1MJ8egBi3b6HV08K9NvxZePBjkafVjA4QQwqK8om4WYTsgcoilo7kjG72OLsF16BJch6Pnsvh220l+Skjh2LmrjFuWyJTVh3ishReDWll2SZVdpy4Su/4I6w+mA6rnXLeQurzcqQnN6lt/zi9TmgYHV8CGD9XdWABHD2g3Glo+B3YVa/IvUbakCBPm5eiubq1fPA6nd0HjjpaOqMzodDpa+dWilV8t0i7f4Iftyfx3+0nSLmczfcMRZmw8QpegOgxs1ei23Uf2n77Eop2p/Lw7lfNZOcb9QfVq0q+FGidQu0Y1czZLCCEqL+9o9bMCTM5xL/xqOzOuVzPeeCSAxTtTmLftJEfSs/g27iTfxp2ktV8tBrXyoUuQp9nW14o/lkHs+iNsPXIeAL0OeoXV5+VOTax2nc1yo2lwdD2sn1A4PKO6C7R+BaKHQTWZxbgqkCJMmF+DFjeLsJ2Vqgi7VZ2a1Xm1S1OGd/JjbWIa38ad5PejGfyamMaviWk09nDi6ZhG9A2vR+YNA19uPc6iXac5ePaK8Xd4ONvTO7wB/Vp4EVy/7MeXCSFElecVoX5eOAZXz4OTh2XjuUfO1WwZ2MqHp2Mase1oBvO2qSVVfj+awe9HM6jvUp2nYhrxZKQ3tZzL/gKepmn8diSDaeuT2H5czc5rq9fxWIsGvNSxCY09KuZSNBZ1Kg7W/RNOblX/tnOCmGHQeiQ4uFk2NmFWUoQJ86vfQvV9Tt1l6UjKnZ2Nnm6h9egWWo8j6Vf4Lu4UCxNSOH7+Kv9cnsjkVQfJNeSTr6nZj+xt9HQJ9qRfCy/a+9eu8ItCCyGEVXNwA48AOH9ILdoc0M3SEd0XnU5H6yYetG7iQWrmdebHneSHHcmcvnSDKasP8fm6JHo+UJ/BrRvxgJdrqZ9P0zQ2HEpn2rojxrHK9jZ6+rf0YlgHP6ubudEsTu9Wd76OrFH/tqkGkc9D21HgXNuioQnLkCJMmF/Bos1WOkPi/WriWYNxvZrx5iMBLNmtJvIouPPV3NuFfhHe9HigHq6O9haOVAghqhDvSFWEJW+32iLsVg1cHXirayCvPNiUFX+eYd62E/yZcomFO1NYuDOFcG9XBrduRPfQelSzvbdxxfn5Gr8mpjF9QxL7Ui8DUM1Wz/9FN2Roe1/qucjsvEWkH1Rjvg4sVf/W2UCLgdD+TXDxsmxswqKkCBPmVy8MdHq4ckZtVYxTNVueim7E/0U1JPH0JY4fOUS3ti2xsZFJNoQQwuy8omDXd+pOWCVS3c6GfhFe9IvwYndyJt/8foLlf55hd3ImuxdkMmH5Af4W1ZD/i25I/btMGmXI11ix9wz/Xn+EQ2nq4qGjvQ0DYxoxpJ2vjFUuzoXjsOlj+HMBaPmADkL7Q8d3oJafpaMTFYAUYcL87J3U+izpiWpyDupbOiKL0Ol0BNatwY2z8t9QCCEsxrhocwIY8sCm8n0nh3u7Ej4gnLGPBvHD9lN8F3eKs5dvMH3DEWZuOsrDwXUY1MqHGF93k+VO8gz5LN59hhkbjnDs/FUAalSzZXBrH55r2xh3J+m5UcTl07B5Cuz8BvLz1L7AHtDpXagTbNnYRIVS+b5phHWo3wLSE9Gd3gVuVbMIE0IIUQF4BKjlUrIvQdo+qB9u6YjKjYdzNUZ0bsqwDn6sSUxj3rYTxB27wMp9Z1m57yz+dZwZ1MqHbs08WXPsGq+t3ULyxesAuDra8Vybxgxu7YOLg6xbVcTVDNj6L9gxB/JuqH1+naHze9AgwrKxiQpJijBhGQ2aw+7v0J3eCW6PWjoaIYQQVZVeD14t4eg61SWxEhdhBWxvmTTq0NkrfLPtBIt2pnI4LYv3luzjvSWF59ZysueF9r48HdMI52ryZ2MRNy7B79MhbgbkZKl9DVtB57+DTxvLxiYqNPnfJCyj/s3JOc7shmDNoqEIIYSo4ryjVBGWvB2iXrB0NGYVULcGH/YN5a2ugSxMSOHbuJMcP38Vt+p6Xu7sz1MxPjjYy5jlInKuQvwX8NvncCNT7asXBp3/AU0eVKtUC3EHUoQJy6gTAjb26K5fxP7aaaC5pSMSwvxuXMbuWhpcy1CLc9o5SOIWwhK8ItVPK1u0uSy5ONjxXNvGPNPah+Pnr5B+4jBRET4yadRf5WVDwtew+RO4mq72eQRA53chqJd8h4sSkyJMWIatvSrETu/EKfMQIF0SRSWnaZBxBJLjb2470J87yANosO6W8+wcVTFm/Onwl31/Pf6X8+wd7/DYmz/18keVECa8WgI6uHgCstLBoZalI7IYvV6HTy0nMpOlmDBhyIM9/4VNk+FSstrn5gMdx6hZD+V7VdwjKcKE5TRocUsRJkQlk52l1sK7WXCRsh2uXzQ5RQfk6+3Q5+cW7sy9pjYyyi82m2olKORuU8AZz3O6c7FoK7OmCStS3UXN2nvugOqS6G/964WJMpKfD/sXwYaJcOGo2lejHnR4C5oPBBuZpETcHynChOXcHBdWM327uvroIetmCCulaZB5Uv3xlrxdFV5p+0EzmJ5nW1197r2jwDsKQ/0IdielEv5AKDb5OZB7/WYRdh1yr978eeu+W37mFLPP5PxijhcwZKutYBxDedDb3uWOnAM6W0dq6b0hPLz84hCipLwjVRGWIkWYQH2vH1oJmyapWTMBHGtB21EQ+bz6HhOiFKQIE5bTuD2anSMOWSfRZkRB86dlBXlhHXJvqEllCgqulB2QlVb0vJpexoIL7yioE2p6h8hgAFJVNxY7ZzUurLxompo2+dbCLOfqbQq4OxRyt56Xc+0v+67eXJQUtT5O9mW13YYeaKivhvboGyDjToSleUertZ2SK9eizeIeaRoc20jA1nexyTyg9lVzgdYjIWYYVKth2fhEpSFFmLAcV2/yBy8n6+e3cTm3Qw103f1faPmcutJUo46lIxRCuXy68C5XynY4vRtu7UIIoLdTM2MVFFxeUeDSwCLhFkunK7wrhXv5PIemgSG3xIVcfnYWR6464aeXVCQqAK+bizaf3qU+x6Jq0TQ4uh42TcYmOQ5nQLNzRBf9IrR+BRzL6XtTVFmS+YRl1QvnSMzHhLtfx2bjRDj5G8TPUlcjo16ANq/JF58wL0MunN1bWHAlby8chH0rJ0/Tgqt+uHRP0enUnT5be3BwvevpmsHAld27yz0sIUqkVhOo7qq66abtQ43aFJWepkHSr7DpY0hNULtsqnGuYXdq9ZmIjUt9CwcoKispwkTF0LAVPLMCjm2E9RMg9Q+19saOr6DVcGj1sho4LURZu3retOBK3Ql5103P0emhTjPVXcnrZuHl5iNTEYsirl69ymeffcbKlSu5dOkSvr6+DB06lEcfvfsMsIsWLWLMmDHFHtu6dSu1a9cu63DFrfR6NVX9kTXoUnaAfZSlIxLlKT8fDv0CmyfDmT1qn60DtHyW/JiXST6aRi1n6ZEjyo8UYaLi0OnArxP4doTDq2HDBHVHYtPHakHENq9A1IvlO25GVG75Bkg/UDiOKzkeLhwrel5118I7XN5RaiZPGQcgSmDkyJHs3buX0aNH4+Pjw/Llyxk1ahT5+fn07NmzRL9j0qRJ+Pr6muxzdXUth2hFEd5RcGSNuijjK0VYpZRvgMSf1Tpf6fvVPjsnNdlG65Hg7HlzvG4x43yFKENShImKR6eDgK7Q9GE4sBQ2ToJzB2HdB7BtBrQbpcaNVfWuX+LurmdCyh8373LFQ0oC5Fwpel7tQHUF3DtabbWaqKviQtyDTZs28dtvvzF16lR69OgBQExMDKdPn2by5Ml07969RAvfNm3alNDQ0PIOVxTn5qLNupQd4HuXc4V1yTfAvkWweQqcv7k0jn0NiB4KMS+DU9VdG05YhhRhouLS66FZHwjqCfsWqjU6Lh6H1WPh91ho/wY0HyTrEQlF0+B8UmHBlbxDFe9opufZO0ODiMKCyysCHNwsErKoXNasWYOjoyNdu3Y12f/YY48xevRo9uzZQ4sWLSwUnSiRBhGg06O7lIztjXJcq0+YjyEX9v5P3fkqWOerugtEv6RmO5Tvf2EhUoSJik9vAw88Ac36wp7vC1erXzFajRvr8DY88CTYyMe5SinBYsgAuDW+WXDdvNPlGaw+U0KUsaSkJPz8/LC1Nf0uCggIMB4vSRE2bNgwLly4QI0aNYiKiuKVV17B39//vmIyGAx3P+kOj7vfx1stOyf0tYPQpe/H+eJ+DIaOlo7IIirF+2/IQffnD+i2foou8yQAmoMbWvRwtMgXoHrNm+cVbWOlaH8pSPtL1/6SPk7+ahXWw8YOWgyCBwao2RM3T4HMU/Dzy7DlX9BpLDR7TLqRVUbFLoa8r3BNqgJ/WQwZryhwlskMhHlkZmbi5VV0nUMXFxfj8Tvx8PBg2LBhhIeH4+zszOHDh5k9ezYDBgzg+++/JzAw8J5j2rt37z0/piwfb40aOvhSm/147Z/JuSunyPB+hFyHqvk9Yo3vv86Qg0fySuoc+Z5q19MByLV3Jc3vCc759Cbf1gEOFjMWuBjW2P6yJO0v3/ZLESasj201NX19+FPwx5ew9VPVxWDh87BlqirGAnvIzHXW7K+LISdvh6vpRc+722LIQtyn+Ph4Bg0aVKJzlyxZQlBQEAC6O3zv3OkYQPv27Wnfvr3x35GRkXTo0IGePXvy+eefM3PmzBLFc6vQ0NASjUP7K4PBwN69e+/78Vat1kto/91AtetpNDj0FfUPzQW/Tmhh/4cW0F1d7KnkrPL9z72Obuc8dNti0V05A4DmXAet1Uj0Ec9Qz86ReiX8VVbZ/jIk7S9d+wsefzdWW4TJNMACe0c1k1HEM2ptsd9iIT0RFjwN9cKh83vQpIsUY9bg8mk4nVBYdJ3Zc5vFkB+42bWwAi6GLCqVxo0bM2HChBKdW6+e+tPO1dW12Ltdly5dAgrviN0LLy8vIiIi2LNnzz0/FsDGxqZUf0SV9vFWyacNhtcTSV49nUYXtqI79TscXY/u6Ho1c2ro49D8Zp6p5PnFKt7/nKvwx1fw27TCi3U1G0Cb19C1GIiuFJN4WUX7y5G0v3zbb7VFmEwDLIyq1YD2b0LkENj2b4ibqe6izH9c/cHe+T1o3P6uv0aYUe519UfN/p8JObIBm+vF3OVyqm1acMliyMKMPD096d+//z09xt/fn+XLl5OXl2cyLuzw4cOAmvXwfmiahl66WZuXvTMZ3l3x7vkONpdOwu7/wu7v4XIK7JijNs9m0Pwp1UXeycPSEVc92Vdg+39g23S4dnMSFZeGagbl8P9TvWaEqMCssgiTaYBFsRzcVMEVPQx++0x9OSfHw7yeqgjr9B40jLZ0lFVXzlVI+hUSl6p14HKvogeqAZpOj04WQxZWrkuXLvz444/8+uuvdO/e3bh/8eLFeHp6EhYWds+/Mzk5mZ07d9K6deuyDFXcC3dflVs6joHjm2DXd3BguVpjavVYWPMP8O+q7o41eUgmiSpv1zPV2qFxM+BGptrn1ljNmPzAADV+XAgrYJXfFDINsLgjJw94eAK0GqHGiP0xF45vhuMPq7XHOo2F+s0tHWXVcOOyKrgSl8CRdZB3vfBYTS/yg3pyROeHX/v+2Di6WipKIcpEhw4daNOmDePGjSMrK4uGDRuyYsUKtmzZwpQpU0wuDo4dO5YlS5awZs0aGjRQ3WqfeeYZWrZsSWBgIE5OThw+fJg5c+ag0+l49dVXLdUsUUBvA36d1Xb9olo6Zdd8NUvrweVqc/KEsAEQ/jR43vtEKuIOrl1QhVf8F5B9We2r1VT1hAnpJ8WvsDpW+YmtiNMAiwqoRl3oPkWNG9s8RSXLpF/VFtQTOo6FOsGWjrLyuXYBDq1UC20fXQ+GnMJjbj4Q3Ftt9Vug5edzZfdu1aVUiEogNjaWTz/9lGnTppGZmYmvry//+te/ioxXzs/Px2AwoGmF69j5+/uzcuVKvvrqK7Kzs3F3dycmJobhw4fTuHFjczdF3ImDm+oCHzkE0hJh93z4c4Eak/R7rNoaRKi7YyH91LpU4v5cPa9ezx1zICdL7asdBB3ehOA+suSIsFpWWYRVxGmAZS2W+2OW9tdoAI9+Bq1eQbd5Mrq9/0N3YBnageVoIf3Q2r8FtZqU3/PfQaV5/6+eR3doBboDS+HEFnT5ecZDWq2maEG90IJ6QZ2Qwi6GN/8IhUrQ/vsk7TfPWizm5OTkxHvvvcd77713x/M++ugjPvroI5N9Y8eOLc/QRHmpEwyPfAhdxqmLfLvmQ9JqSE1Q26ox6sJf+FPQuIMso1JSV9Lg92lq0o3ca2pfnVDo8JaaAVleR2HlLF6EVZZpgGUtltIxW/t9hlG9VlfqH/oatzOb0e37CW3/IjK8HuaM/yByHOuaJ46/sMb33/ZGBm5nt+J6ehM1Mv5ER+GaXddq+JJZvz0X67XnRg0ftfOsAc4WP8ObNba/LEn7q3b7RSVhYweBj6otK13dGds1H84dgL3/U5tLQwj/m5o4ws3H0hFXTJdS4bfPYec8yLuh9tVvDh3eVmPvZKywqCQsXoRVlmmAZS2W+2OZ9odDuz4YzvyJftMkdEmr8UheRa3UdWjNB6K1HQU165slEqt7/y+loDu4DN2BZZAcj47CrlRavXC0wJ5oQT2pVqsJdYA6d/l1Vtf+MibtN89aLEKYnbOn6grfaoQaM7ZrPuz9CS6dgk0fq82nnbo7FtwL7J0sHbHlZZ5S637u+q6wG7tXlCq+mjwoxZeodCxehFWWaYBlLZbSsUj7vZrDUz9C8g7YMAHdsY3oEr5Sffsjh0Db18HZPGvGVej3/8JxNb4r8WfVteZWXpFqfFdQT3RuPtxviqzQ7TcDaX/Vbr+oxHQ6NTasQYTqsnhwhSoyjm2EE1vU9subENJXTebhHVX1io0Lx2DLv2DP91DQlb1RWzXmq3GHqvd6iCrD4kXY/ZBpgEWZ8o6EQT/Dia2wfgKc2gZx/4aEryH6RXU109Hd0lGa1/kkVXQl/gxn/7zlgA4atjIWXrJYshBClJCdg1roOfRxyEyGPT/A7u/g4gnY+Y3aajVVXRXD/gY161k64vJ1/ghs+QT+/BG0m+M7G3dQY7582lo2NiHMwCqLMJkGWJQLn7bw7Eo4uk4VY6d3wdZ/qRmZWo2AmJegek1LR1k+NA3SD6ii68BSSE8sPKazUa9NcC8I7Ak17tbJUAghxB25eqs7Pe1Gw6nfVXfFxCWQkQTrxsP6f0KTLqq7YkC3yrXwcPoB2PwJ7F8E2s2xxE0eUsWXd5RlYxPCjKyyCAOZBliUE51OJT6/B+HQL7D+Q7Ug58aJED8T2rwGUS9Ujv77mqbuchXc8co4UnhMbwu+HdUdr4BHwamWxcIUQohKS69XF7l82kL3ybB/ieoSf2pb4ZIqDm4Q+oSa7r7eA5aO+P6d3auWi0lcCgXjiQO6q0WWG0RYNDQhLMFqizCZBliUK51OzXDl3w0SF8OGSeoK5dr3Ydu/1dXLiGfArrqlI703mqbGdRUUXpknC4/ZVFODn4N6QUBXlfiFEEKYR7Ua0GKg2s4fUcXYnh/gymnY/oXa6oaqsWMPPGE93eRP74JNU+DQisJ9Qb3UIsvWXFQKUUpWW4QJYRZ6vVpoM6i3ml544yRVuKx6W61f0v5NdXXSxs7Skd5efj4kx9/sargMLqcUHrN1gKYPqTteTR+uvN0thRDCmng0gS7vQ+f34OgGNXbs4Ap1N2nV2/DrexDYXRVkfp3BpgL+OZe8AzZPVnfzANBByGPQ7g21tpoQVVwF/F8rRAVkY6vWdgl9XM1stXkKXE6F5a/Bb59Bh3fUlUl9BZnhzZCnxhkUFF5ZaYXH7J3B/xFVeDXpUjm6VgohRGWkt4GmXdR27YKa5n73d3BmT2GPBue6EPakuiDocX+zQ5epk7/DpslwbIP6t06vulO2Gw21/S0bmxAViBRhQtwLGzto+ayauSrha9gyVc1stWSYmsSj4xgI7qPuoJmbIReOb1L97Q8uh2sZhcequairpkG91FVTa+tGKYQQVZ2jO0QPVdvZfaq74p8LIOusuhj422dqXa3mT0Ozvubt2aBparr9TZPVT1Bji8OehLajoJaf+WIRwkpIESbE/bCrDjHDVN/97f9Rye/8YfjpWagzFTq9q2a0Ku/1TfKyVVeVxJ/VRCI3MguPObircW3BfaBxe7C1L99YhBBCmEfdEOg6CbqMh8OrVEGWtAZStqtt5duqt0Pzp9SaW+V1YVDT4Oh6VXwlx6l9ejtVCLZ9Hdwalc/zClEJSBEmRGnYO0Hb16DlcxA3E7ZNh7R98MPfoH4L1Z/fr3PZFmM519Q0+ok/w6FVkHOl8JiTJwT1UMm3UduKOU5ACCFE2bC1V8uHBPeCK2fVnbFd8+H8IfjzB7W5NlJT3Yf/DVwbls3zahocXq3GfKUmqH021SBiMLR5FVy8yuZ5hKjE5C80IcpC9ZrQ8W01ff3vsRA/C07vhO8eg4atVTHm0+b+f392FiStVl0Nk36F3GuFx2rUVwk4qBc0jKk449KEEEKYT426qgBq/Qqk/KHGju1bpCaT2jhRTSzVuL26SxXUUy0efa/y89Ush5smqyVOQE3w1PI5aPOKikEIUSJShAlRlhzd1YxWMcNh66dqoedTv8PX3dW6W53eA+/Ikv2uG5fUna7En9Wdr7wbhcdcG6qiK7iPWl/FEmPQhBBCVDw6ncoz3pHwyCQ1RnjXd2rMcMFWzUXNVNj8aZVD7tZbI9+gctHmT9TamQB2ThA1BFqNBOfa5d8uISoZKcKEKA/OtaHrRGg9QiWtnd/AsY1q8++qxowVtz7KtQtqGuIDS9VYr/zcwmPufqqbYXAvqBde/uPNhBBCWDd7RzVz7wNPwMWTsOd7NX4s8xQkzFWbR4AaO/bAk1Cjjunj8w2wf5GaEfj8IbWvWk2IGqouNjrVMn+bhKgkpAgTojzVrA89/qW6aWyaAnv+qwZRH16lCqr2b2ObfQFdwtdwcBkc3wyaofDxtQNvFl69wTNYCi8hhBD3x60RdHwH2r8FJ7eqsWOJP6vias0/YO14tV5k86egcUdqJa9C/9sLcOGoenx1F1V4Rb8IDm4WbYoQlYEUYUKYg5sP9Pm3mi1q4yTYtxASf0afuJQH0KEjv/DcuqFqcejgXlA7wGIhCyGEqIT0ejU2rHF76D4Z9i9WBVnKdji8Eg6vRK+zwafggqCDO7R6Wd39Mue090JUclKECWFOHk3g8S+h3SjYMBHdweWAhla/BbqCrobuvpaOUgghRFVQ3QUinlHbucOqq+KeH9BlnSXX3g2b9q+hjxwC1ZwtHakQlY4UYUJYQp1m8OR8DBnH2X/gIM1aPYyNjcxqKIQQwkJq+8ND46Hz3zGkH2DvqcuEtYwByU1ClAuZUk0IS3JtSK6Dp6WjEEIIIRQbW/AMRrOtbulIhKjUpAgTQgghhBBCCDOSIkwIIYQQQgghzEiKMCGEEEIIIYQwIynChBBCCCGEEMKMpAgTQgghhBBCCDOSIkwIIYQQQgghzEiKMCGEEEIIIYQwIynChBBCCCGEEMKMpAgTQgghhBBCCDOSIkwIIYQQQgghzEiKMCGEEEIIIYQwIynChBBCCCGEEMKMpAgTQgghhBBCCDOSIkwIIYQQQgghzMjW0gFYO03TADAYDPf1+ILH3e/jrZ20X9p/68+qRtpfuvYXPK7ge1gUktxUOtJ+af+tP6saab95cpNOk+xVKjk5Oezdu9fSYQghRJUVGhqKvb29pcOoUCQ3CSGEZd0tN0kRVkr5+fnk5eWh1+vR6XSWDkcIIaoMTdPIz8/H1tYWvV56199KcpMQQlhGSXOTFGFCCCGEEEIIYUZy6VAIIYQQQgghzEiKMCGEEEIIIYQwIynChBBCCCGEEMKMpAgTQgghhBBCCDOSIkwIIYQQQgghzEiKMCGEEEIIIYQwIynChBBCCCGEEMKMpAgrR4sWLSIgIMC4BQcH07ZtW15//XVOnDhhcu4ff/zBu+++y2OPPUZISAgBAQGkpKRYJvAyUtL2GwwG5s6dy/PPP0/79u0JCwujW7dufPLJJ1y+fNlyDSgnf31d/rrFx8dbOsT7tmrVKgICAvjll1+KHOvVqxcBAQFs2bKlyLEuXbrQt29fADZs2MBbb71Fz549adasGQEBAeUed1kpbfuzsrKYOXMmAwcOpE2bNjRv3pyePXsye/ZssrOzzdGEUimL9//TTz+lT58+REVFERoayoMPPsjf//53UlNTyz3+qkJyk+Sm4khuktz0V5Kbyjc32d73I0WJTZo0CV9fX7Kzs9m5cyezZs0iPj6elStX4uLiAkBcXBzbtm0jKCgIJycntm/fbuGoy87d2n/jxg1iY2Pp0aMH/fv3x83NjcTERGbOnMmGDRtYuHAh1atXt3QzylzB6/JXTZo0sUA0ZSMqKgqdTkdcXBzdu3c37s/MzOTw4cM4OjoSHx9Pu3btjMfOnj1LcnIyzz77LABr1qxhz549BAUFYWdnx/79+83ejvtV2vafPn2aefPm0bt3b5555hkcHR1JSEhg+vTp/P7778ydOxedTmeJppVIWbz/ly9f5tFHH8XPzw8nJyeOHDnCzJkzWb9+PcuXL8fNzc3s7aqsJDdJbiqO5CZFcpPkpvLOTVKEmUHTpk0JDQ0FIDo6GoPBQGxsLGvXrqVfv34ADB8+nBEjRgDw5ZdfVqpEd7f2V69enXXr1pl8gKOjo6lXrx6vvvoqq1evpnfv3pYKv9zc+rpUFu7u7jRt2rTI53fHjh3Y2trSr1+/IldT4+LiAPWeA0yYMAG9Xt2k/+CDD6wq0ZW2/V5eXqxfvx5HR0fj8VatWuHg4MDkyZNJSEigZcuW5d+Q+1QW7//7779vcrzgdRk6dCjr1q3j8ccfL8cWVC2SmyQ3FUdykyK5SXITlG9uku6IFlDw5ZaRkWHcV/Afuyr4a/ttbGyKvYLwwAMPAOpqhLAe0dHRHD9+nPT0dOO++Ph4QkJC6NChA/v37ycrK8t4bPv27djY2Bi/wK39/0Jp2u/o6GiS5ApY0/+F0r7/xXF3dwfA1lauG5YnyU2SmyozyU2SmypabrLuT5SVKuhP7+PjY9lALKSk7S+4CmHNXSDuJD8/n7y8PJPNYDBYOqxSi4mJATC54hQfH09UVBQtWrRAp9ORkJBgciw4OJgaNWqYPdbyUB7tt6b/C2XV/ry8PG7cuEFiYiITJ07Ex8eHhx56yDyNqKIkN0luAslNtx6T3CS5qTxzkxRhZlDwhXb16lW2bNnCzJkziYyMpHPnzpYOzSzup/1paWlMnTqVkJAQOnXqZMZozeeJJ56gWbNmJltl6AISGRmJXq83ftFdvHiRpKQkIiMjcXJyIjg42PjFfebMGVJSUoy3+yuDsm7/wYMHmTNnDg899BCBgYFmaUNplEX7z507R7NmzQgLC6Nv374YDAa++eYbnJyczN6eykxyk+Sm4khuktwkuck8uUn6dpjBE088YfJvPz8/ZsyYUWW61txr+zMzM3nhhRfQNI3PPvvM6rsA3M7HH3+Mn5+fyb6KPLC1pFxcXAgMDDT2r96xYwc2Nja0aNECUF+EBV90BedUpkRXlu1PSUlh2LBh1K1blwkTJpgh+tIri/a7ubnx008/kZOTw7Fjx5gzZw6DBg3i22+/xdPT04ytqdwkN0luKo7kJslNkpvMk5sq5zdIBfPxxx/z008/MW/ePAYMGMDRo0cZNWqUpcMym3tp/6VLl3juuedIS0vjq6++wtvb28zRmo+fnx+hoaEmW0hIiKXDKhPR0dGcOHGCtLQ04uPjadasmfFKUVRUFAcOHODKlSvEx8dja2tLRESEhSMuW2XR/tTUVAYNGoSNjQ3z5s3D1dXVzK24f6Vtv62tLaGhoURERNC/f3/mzZtHSkoKs2fPtkRzKi3JTZKbiiO5SXKT5Cbz5CYpwsyg4AstJiaGDz74gP79+7NlyxZWrVpl6dDMoqTtv3TpEs8++ywpKSnMnTvXKm5vi+IVXD3avn0727dvJzIy0nis4Ettx44dxMfHExoaWum6mZW2/ampqQwcOBCAb775hrp165op8rJR1u9/3bp18fT0LLKGlSgdyU2Sm6oayU2Sm6Di5CYpwizgzTffxMXFhWnTppGfn2/pcMyuuPYXJLnk5GS+/PJLgoODLRylKI3IyEhsbGxYvXo1SUlJREVFGY/VqFGDoKAglixZQmpqaqXq7lGgNO0/ffo0AwcOJD8/n3nz5tGgQQNzh19qZf3+nzx5krNnz9KoUaPyDLvKk9wkuamyk9wkuaki5aaq0fG7gnFxcWHo0KFMmTKFZcuW0bt3by5cuGAcLHj48GEANm/ejLu7O+7u7iYfFGv31/Y/8sgjPP/88yQmJjJ27FgMBgO7d+82nu/u7k7Dhg0tF3A5SUpKKnbGqYYNGxqnPbVWzs7OBAcHs3btWvR6fZFb+pGRkcybNw8o2uc6NTWVvXv3AnDq1CkA45XpBg0aWMUA8fttf0ZGBoMGDeLcuXN8+OGHZGRkmEwXXrduXau48ni/7T948CCTJk3ikUcewdvbG71ez+HDh/n6669xdXXlueeeM2s7qhrJTZKbQHKT5CbJTWCe3CRFmIUMHDiQ+fPnM2PGDHr06EFSUhKvvvqqyTnjx48HVD/Vb7/91hJhlptb29+8eXPjF9uHH35Y5Ny+ffvy0UcfmTvEcjdmzJhi90+YMIH+/fubOZqyFx0dzd69ewkKCsLZ2dnkWGRkJF9//TV2dnY0b97c5Fh8fHyR16bg/4Y1fRbup/1HjhwhOTkZUFfl/2rEiBGMHDmyfAMvI/fTfg8PDzw9PZk7dy7nzp0jLy+PunXr0rFjR4YNG0a9evXM3YwqR3KT5CbJTZKbJDeZJzfpNE3TStUaIYQQQgghhBAlJmPChBBCCCGEEMKMpAgTQgghhBBCCDOSIkwIIYQQQgghzEiKMCGEEEIIIYQwIynChBBCCCGEEMKMpAgTQgghhBBCCDOSIkwIIYQQQgghzEiKMCGEEEIIIYQwIynChNVZtGgRAQEBxi04OJi2bdvy+uuvc+LECUuHB8CsWbNYu3Ztkf3x8fEEBAQQHx9vgaiU9evXM2zYMFq3bk1ISAhRUVEMHjyYpUuXkpuba7G4/qq41+qdd96hc+fO5fq8aWlpxMbGcuDAgXJ9HiFE5SK5qXQkN92Z5KbKx9bSAQhxvyZNmoSvry/Z2dns3LmTWbNmER8fz8qVK3FxcbFobF988QWPPPIIXbp0MdnfrFkzFixYQJMmTcwek6ZpjB07lkWLFtGhQwfeeecd6tWrx5UrV4iPj2f8+PFcvHiRwYMHmz22kho+fDiDBg0q1+dIT09n+vTpNGjQgKCgoHJ9LiFE5SO56d5IbioZyU2VjxRhwmo1bdqU0NBQAKKjozEYDMTGxrJ27Vr69etn4eiK5+zsTHh4uEWee86cOSxatIiRI0cyYsQIk2OdO3dmyJAhnDx50qwx3bhxg+rVq5f4/IYNG5ZjNEIIUXqSm+6N5CZRVUl3RFFpFCS9jIwMk/179+5l2LBhREVFERoaSp8+ffjll19Mzrlw4QLjxo2je/fuNG/enFatWjFo0CD++OOPIs+Tk5PD9OnT6datG6GhoURHRzNw4EB27twJQEBAANeuXWPx4sXGbikDBw4Ebt/lY926dQwYMICwsDCaN2/Os88+y65du0zOiY2NJSAggKSkJEaNGkVERAStW7dmzJgxXLly5Y6vTW5uLnPmzMHX15eXX3652HNq165Ny5Ytjf/OzMxk3LhxtGvXjpCQEB588EE+/fRTcnJyTB6XnZ3N1KlT6dy5MyEhIbRr147x48dz+fJlk/M6d+7Miy++yK+//kqfPn0IDQ1l+vTpABw9epTnn3+esLAwoqOj+cc//sHVq1eLxFhcl4+AgAA++OADlixZQrdu3QgLC6NXr15s2LDB5LyTJ08yZswYHn74YcLCwmjXrh3Dhg3j0KFDxnPi4+N5/PHHARgzZozx/YuNjTWeU5LPkxBCFJDcdHuSmyQ3VWVyJ0xUGikpKQD4+PgY98XFxTFkyBDCwsIYN24cNWrU4JdffuH111/nxo0bPPbYY4D6UgcYMWIEHh4eXLt2jTVr1jBw4EC+/vproqOjAcjLy2PIkCEkJCQwaNAgYmJiMBgM7NmzhzNnzgCwYMECBg8eTHR0NMOHDwfUVcbbWbZsGW+88QZt27Zl6tSp5OTkMGfOHONz35p8AEaOHEn37t15/PHHOXz4MFOnTgVUF5jb2bdvH5mZmfTv3x+dTnfX1zI7O5tBgwaRnJzMyJEjCQgI4I8//mD27NkcOHCA2bNnA6obyfDhw4mLi2Po0KG0bNmSQ4cOERsby+7du1mwYAH29vbG37t//36OHj3KSy+9hJeXFw4ODpw/f56BAwdia2vL+++/T61atVi2bBn//Oc/7xpngY0bN7J3715eeeUVHB0dmTNnDiNGjGDVqlV4e3sDqiuHq6sro0ePxt3dnUuXLrF48WKeeOIJFi9ejK+vL82aNWPSpEmMGTOGl156iY4dOwJQt25doOSfJyGEKCC5SXKT5CZRLE0IK7Nw4ULN399f2717t5abm6tlZWVpmzdv1tq0aaM99dRTWm5urvHcrl27an369DHZp2ma9uKLL2pt2rTRDAZDsc+Rl5en5ebmaoMHD9Zefvll4/7Fixdr/v7+2o8//njHGMPDw7W33367yP64uDjN399fi4uL0zRN0wwGg9a2bVutR48eJrFkZWVprVq10gYMGGDcN23aNM3f31/7z3/+Y/I7x40bp4WGhmr5+fm3jWfFihWav7+/9v33398x7gLff/+95u/vr/3yyy8m+2fPnq35+/trW7du1TRN0zZv3lxsTAXPt2DBAuO+Tp06aUFBQdqxY8dMzp0yZYoWEBCgHThwwGT/s88+a/JaaZqmvf3221qnTp1MzvP399dat26tXblyxbjv3LlzWmBgoPbFF1/cto15eXlaTk6O9vDDD2sTJ0407v/zzz81f39/beHChUUec7+fJyFE5Se5SXLTrSQ3ibuR7ojCaj3xxBM0a9aMFi1aMGTIEGrWrMmMGTOwtVU3eE+ePMmxY8fo2bMnoK4UFmzt27fn3LlzHD9+3Pj7vv/+e/r27UtoaCjBwcE0a9aMbdu2cfToUeM5W7ZsoVq1amXWr//48eOkp6fTu3dv9PrC/45OTk48/PDD7Nmzh+vXr5s8prguD9nZ2UW6upRGXFwcjo6OdO3a1WR/wdW0bdu2Gc+7dX+Bbt264ejoaDzv1lgbN25ssi8+Pp6mTZsSGBhosr9Hjx4ljjc6Otrkiq6Hhwe1atUiNTXVuC8vL49Zs2bRvXt3QkJCCA4OJiQkhBMnTpi8x7dzr58nIUTVJLlJkdwkuUncmXRHFFbr448/xs/Pj6tXr/LLL7+wYMECRo0axZw5cwA4f/688byPP/642N9x8eJFAObOnctHH33Ek08+yauvvoqbmxt6vZ7PP/+cY8eOGc+/cOECnp6eJkmpNAqev3bt2kWOeXp6kp+fz+XLl3FwcDDud3V1NTmvoEvFjRs3bvs89erVAwq7xdxNZmYmHh4eRbqH1KpVC1tbW2MXmczMTGxtbXF3dzc5T6fT4eHhYTyvQHHtzMzMxMvLq8h+Dw+PEsUKRV8TUK9Ldna28d8fffQR8+fP54UXXiAyMhIXFxd0Oh3vvfeeyXm3cy+fJyFE1SW5SZHcJLlJ3JkUYcJq+fn5GQc8x8TEkJ+fz//+9z9WrVpF165dcXNzA+DFF1/koYceKvZ3FFz5Wrp0KVFRUYwfP97k+F8H4Lq7u5OQkEB+fn6ZJLuCGM+dO1fkWHp6Onq9npo1a5b6eUJCQnB1dWXdunWMHj36rn3vXV1d2bNnD5qmmZybkZFBXl6eMW5XV1fy8vK4cOGCSbLTNI3z588b358CxT2vq6urMYncqrh9pbF06VL69OnDqFGjTPZfvHixRK/xvXyehBBVl+SmkpPcJLmpKpPuiKLSePPNN3FxcWHatGnk5+fj6+uLj48PBw8eJDQ0tNitoJuATqczGaQLcPDgQXbv3m2yr127dmRnZ7No0aI7xmJvb3/Hq38FGjduTJ06dVi+fDmaphn3X7t2jV9//ZXw8HCTK433y87OjiFDhnDs2DH+/e9/F3tORkYGCQkJALRq1Ypr164VWdRzyZIlxuO3/ly6dKnJeatXr+batWvG43cSHR1NUlISBw8eNNm/fPnyuzfsHuh0Ouzs7Ez2bdy4kbS0NJN9t7t6ey+fJyGEKCC56fYkN0luqsrkTpioNFxcXBg6dChTpkxh2bJl9O7dm/Hjx/PCCy/w/PPP07dvX+rUqcOlS5c4evQo+/fvZ9q0aQB07NiRGTNmMG3aNCIjIzl+/DgzZszAy8sLg8FgfI4ePXqwaNEixo0bx/Hjx4mOjkbTNPbs2YOfnx+PPvooAP7+/mzfvp3169dTu3ZtnJyc8PX1LRKzXq/nzTff5I033uDFF19kwIAB5OTk8OWXX3L58mVGjx5dZq9PQaKLjY1l79699OjRw7gg5o4dO/jxxx8ZOXIkERER9OnTh/nz5/P222+TmpqKv78/CQkJfPHFF3To0IHWrVsD0KZNG9q2bcsnn3xCVlYWLVq04NChQ0ybNo3g4GB69+5917gGDx7MwoULGTp0KK+99ppxBqpbu9qUhY4dOxpnmgoICGD//v18+eWXxtmlCjRs2JDq1auzbNky/Pz8cHR0xNPTkzp16pT48ySEEAUkN92Z5CbJTVWVFGGiUhk4cCDz589nxowZ9OjRg5iYGP73v/8xa9YsJk6cyOXLl3F1dcXPz49u3boZHzds2DCuX7/OTz/9xJw5c2jSpAnjxo1j7dq1bN++3Xiera0t//nPf/jiiy9YsWIF8+bNw8nJicDAQNq1a2c8791332X8+PGMGjWK69evExUVxbfffltszD179sTBwYHZs2fz+uuvY2NjQ1hYGN988w0tWrQos9dGp9MxadIkunTpwo8//mh8PQrif+ONN4yDmKtVq8Y333zDp59+ypw5c7h48SJ16tThueeeM1lMU6fTMWPGDGJjY1m0aBGzZs3C1dWV3r17M2rUqCJXcItTu3ZtvvvuOz788EPGjRuHg4MDXbp04e9//7txGuWy8O6772Jra8vs2bO5du0awcHBxMbG8vnnn5uc5+DgwMSJE5k+fTrPP/88ubm5jBgxgpEjR5b48ySEELeS3HR7kpskN1VVOu3W+8xCCCGEEEIIIcqVjAkTQgghhBBCCDOSIkwIIYQQQgghzEiKMCGEEEIIIYQwIynChBBCCCGEEMKMpAgTQgghhBBCCDOSIkwIIYQQQgghzEiKMCGEEEIIIYQwIynChBBCCCGEEMKMpAgTQgghhBBCCDOSIkwIIYQQQgghzEiKMCGEEEIIIYQwo/8HfGUc9jPH+soAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "%matplotlib inline\n",
+ "\n",
+ "sns.set_style(\"whitegrid\")\n",
+ "plt.rcParams[\"font.size\"] = 12\n",
+ "\n",
+ "for mof_name, mof_df in df.groupby(\"mof_name\"):\n",
+ " # Sort by region and distance\n",
+ " df_co2 = mof_df[mof_df[\"gas\"] == \"CO2\"].sort_values([\"region\", \"dist\"])\n",
+ " df_h2o = mof_df[mof_df[\"gas\"] == \"H2O\"].sort_values([\"region\", \"dist\"])\n",
+ " region_order = {\"R\": 0, \"E\": 1, \"P\": 2}\n",
+ " df_co2 = df_co2.sort_values(by=[\"region\", \"dist\"], key=lambda x: x.map(region_order))\n",
+ " df_h2o = df_h2o.sort_values(by=[\"region\", \"dist\"], key=lambda x: x.map(region_order))\n",
+ "\n",
+ " # Plot\n",
+ " fig, axes = plt.subplots(1, 2, figsize=(10, 5)) \n",
+ " # CO2\n",
+ " ax = axes[0]\n",
+ " sns.lineplot(data=df_co2, x=[\"R1\", \"R2\", \"E\", \"W1\", \"W2\", \"W3\"], y=\"true_ie\", ax=ax)\n",
+ " sns.lineplot(data=df_co2, x=[\"R1\", \"R2\", \"E\", \"W1\", \"W2\", \"W3\"], y=\"pred_ie\", ax=ax)\n",
+ " ax.set_title(f\"{mof_name} - CO$_2$\")\n",
+ " ax.set_ylabel(\"Intermolecular Energy (eV)\")\n",
+ " ax.set_xlabel(\"Reaction Coordinate\")\n",
+ " # H2O\n",
+ " ax = axes[1]\n",
+ " sns.lineplot(data=df_h2o, x=[\"R1\", \"R2\", \"E\", \"W1\", \"W2\", \"W3\"], y=\"true_ie\", ax=ax)\n",
+ " sns.lineplot(data=df_h2o, x=[\"R1\", \"R2\", \"E\", \"W1\", \"W2\", \"W3\"], y=\"pred_ie\", ax=ax)\n",
+ " axes[1].set_title(f\"{mof_name} - H$_2$O\")\n",
+ " axes[1].set_ylabel(None)\n",
+ " axes[1].set_xlabel(\"Reaction Coordinate\")\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "mlip-arena",
+ "language": "python",
+ "name": "mlip-arena"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "state": {},
+ "version_major": 2,
+ "version_minor": 0
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/benchmarks/mof/structures/Al-MIL53.cif b/benchmarks/mof/structures/Al-MIL53.cif
new file mode 100644
index 0000000000000000000000000000000000000000..6467a56520f2d1359288706c78546576abf42146
--- /dev/null
+++ b/benchmarks/mof/structures/Al-MIL53.cif
@@ -0,0 +1,218 @@
+data_SABVOH_manual
+_audit_creation_date 2014-07-02
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 17.1290
+_cell_length_b 6.6284
+_cell_length_c 12.1816
+_cell_angle_alpha 90.0000
+_cell_angle_beta 90.0000
+_cell_angle_gamma 90.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+O1 O 0.08295 0.92756 0.08189 0.00000 Uiso 1.00
+O2 O 0.43312 0.92799 0.39387 0.00000 Uiso 1.00
+C3 C 0.22872 0.93670 0.19502 0.00000 Uiso 1.00
+C4 C 0.29944 0.93681 0.25417 0.00000 Uiso 1.00
+H5 H 0.20344 0.07928 0.17100 0.00000 Uiso 1.00
+H6 H 0.32573 0.07955 0.27542 0.00000 Uiso 1.00
+O7 O 0.41958 0.08310 0.57835 0.00000 Uiso 1.00
+O8 O 0.07025 0.08486 0.89497 0.00000 Uiso 1.00
+C9 C 0.27318 0.07140 0.69256 0.00000 Uiso 1.00
+C10 C 0.20192 0.07178 0.75098 0.00000 Uiso 1.00
+H11 H 0.29906 0.92886 0.67006 0.00000 Uiso 1.00
+H12 H 0.17610 0.92868 0.77357 0.00000 Uiso 1.00
+O13 O 0.91868 0.42746 0.91654 0.00000 Uiso 1.00
+O14 O 0.56964 0.42801 0.60310 0.00000 Uiso 1.00
+C15 C 0.77142 0.43670 0.80673 0.00000 Uiso 1.00
+C16 C 0.70082 0.43685 0.74719 0.00000 Uiso 1.00
+H17 H 0.79674 0.57957 0.83025 0.00000 Uiso 1.00
+H18 H 0.67486 0.57972 0.72493 0.00000 Uiso 1.00
+O19 O 0.58352 0.58315 0.41688 0.00000 Uiso 1.00
+O20 O 0.93250 0.58439 0.09914 0.00000 Uiso 1.00
+C21 C 0.72987 0.57114 0.30225 0.00000 Uiso 1.00
+C22 C 0.80087 0.57158 0.24361 0.00000 Uiso 1.00
+H23 H 0.70477 0.42860 0.32652 0.00000 Uiso 1.00
+H24 H 0.82731 0.42877 0.22221 0.00000 Uiso 1.00
+O25 O 0.91829 0.08423 0.91904 0.00000 Uiso 1.00
+O26 O 0.57171 0.08591 0.59858 0.00000 Uiso 1.00
+C27 C 0.77155 0.07344 0.80614 0.00000 Uiso 1.00
+C28 C 0.70097 0.07362 0.74645 0.00000 Uiso 1.00
+H29 H 0.79709 0.93047 0.82929 0.00000 Uiso 1.00
+H30 H 0.67514 0.93071 0.72384 0.00000 Uiso 1.00
+O31 O 0.58293 0.92635 0.41246 0.00000 Uiso 1.00
+O32 O 0.93337 0.92749 0.10242 0.00000 Uiso 1.00
+C33 C 0.72994 0.93450 0.30253 0.00000 Uiso 1.00
+C34 C 0.80090 0.93487 0.24420 0.00000 Uiso 1.00
+H35 H 0.70489 0.07746 0.32704 0.00000 Uiso 1.00
+H36 H 0.82734 0.07745 0.22321 0.00000 Uiso 1.00
+O37 O 0.08348 0.58500 0.07900 0.00000 Uiso 1.00
+O38 O 0.43136 0.58522 0.39797 0.00000 Uiso 1.00
+C39 C 0.22857 0.57340 0.19548 0.00000 Uiso 1.00
+C40 C 0.29926 0.57338 0.25488 0.00000 Uiso 1.00
+H41 H 0.20314 0.43077 0.17175 0.00000 Uiso 1.00
+H42 H 0.32534 0.43037 0.27655 0.00000 Uiso 1.00
+O43 O 0.41998 0.42618 0.58233 0.00000 Uiso 1.00
+O44 O 0.06916 0.42744 0.89141 0.00000 Uiso 1.00
+C45 C 0.27313 0.43452 0.69225 0.00000 Uiso 1.00
+C46 C 0.20191 0.43507 0.75039 0.00000 Uiso 1.00
+H47 H 0.29897 0.57742 0.66955 0.00000 Uiso 1.00
+H48 H 0.17617 0.57802 0.77264 0.00000 Uiso 1.00
+Al49 Al 0.00184 0.00815 -0.00109 0.00000 Uiso 1.00
+Al50 Al 0.50246 0.00700 0.49631 0.00000 Uiso 1.00
+Al51 Al 0.00180 0.50804 -0.00372 0.00000 Uiso 1.00
+Al52 Al 0.50149 0.50826 0.49995 0.00000 Uiso 1.00
+O53 O 0.00033 0.75904 0.93824 0.00000 Uiso 1.00
+C54 C 0.40499 0.75581 0.36276 0.00000 Uiso 1.00
+C55 C 0.11410 0.75578 0.10647 0.00000 Uiso 1.00
+C56 C 0.33504 0.75518 0.28738 0.00000 Uiso 1.00
+C57 C 0.19073 0.75512 0.16757 0.00000 Uiso 1.00
+O58 O 0.50752 0.25915 0.43997 0.00000 Uiso 1.00
+C59 C 0.09648 0.25525 0.85941 0.00000 Uiso 1.00
+C60 C 0.38857 0.25366 0.60574 0.00000 Uiso 1.00
+C61 C 0.16579 0.25390 0.78301 0.00000 Uiso 1.00
+C62 C 0.31116 0.25317 0.66510 0.00000 Uiso 1.00
+O63 O 0.00901 0.25908 0.05589 0.00000 Uiso 1.00
+C64 C 0.59727 0.25599 0.63482 0.00000 Uiso 1.00
+C65 C 0.88710 0.25546 0.89295 0.00000 Uiso 1.00
+C66 C 0.66547 0.25545 0.71300 0.00000 Uiso 1.00
+C67 C 0.80956 0.25497 0.83393 0.00000 Uiso 1.00
+O68 O 0.50121 0.75932 0.55804 0.00000 Uiso 1.00
+C69 C 0.90602 0.75505 0.13439 0.00000 Uiso 1.00
+C70 C 0.61417 0.75373 0.38894 0.00000 Uiso 1.00
+C71 C 0.83658 0.75367 0.21089 0.00000 Uiso 1.00
+C72 C 0.69136 0.75312 0.32891 0.00000 Uiso 1.00
+H73 H 0.51416 0.26019 0.36979 0.00000 Uiso 1.00
+H74 H 0.50215 0.76047 0.62887 0.00000 Uiso 1.00
+H75 H 0.01841 0.26004 0.12543 0.00000 Uiso 1.00
+H76 H -0.00035 0.76007 0.86745 0.00000 Uiso 1.00
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_2
+_ccdc_geom_bond_type
+O1 C55 1.293 . A
+O1 Al49 1.799 1_565 A
+O2 C54 1.295 . A
+O2 Al50 1.801 1_565 A
+C3 C4 1.409 . A
+C3 C57 1.409 . A
+C3 H5 1.080 1_565 A
+C4 C56 1.409 . A
+C4 H6 1.079 1_565 A
+H5 C3 1.080 1_545 A
+H6 C4 1.079 1_545 A
+O7 Al50 1.808 . S
+O7 C60 1.293 . A
+O8 C59 1.290 . A
+O8 Al49 1.799 1_556 A
+C9 C10 1.413 . A
+C9 C62 1.410 . A
+C9 H11 1.079 1_545 A
+C10 C61 1.412 . A
+C10 H12 1.082 1_545 A
+H11 C9 1.079 1_565 A
+H12 C10 1.082 1_565 A
+O13 C65 1.294 . A
+O13 Al51 1.804 1_656 A
+O14 C64 1.294 . A
+O14 Al52 1.796 . S
+C15 C16 1.410 . A
+C15 C67 1.410 . A
+C15 H17 1.080 . S
+C16 C66 1.409 . A
+C16 H18 1.081 . S
+O19 Al52 1.801 . S
+O19 C70 1.292 . A
+O20 C69 1.292 . A
+O20 Al51 1.799 1_655 A
+C21 C22 1.410 . A
+C21 C72 1.413 . A
+C21 H23 1.079 . S
+C22 C71 1.411 . A
+C22 H24 1.081 . S
+O25 C65 1.294 . A
+O25 Al49 1.803 1_656 S
+O26 C64 1.287 . A
+O26 Al50 1.798 . S
+C27 C28 1.411 . A
+C27 C67 1.409 . A
+C27 H29 1.081 1_545 A
+C28 C66 1.410 . A
+C28 H30 1.081 1_545 A
+H29 C27 1.081 1_565 A
+H30 C28 1.081 1_565 A
+O31 C70 1.295 . A
+O31 Al50 1.797 1_565 S
+O32 C69 1.295 . A
+O32 Al49 1.803 1_665 S
+C33 C34 1.408 . A
+C33 C72 1.409 . A
+C33 H35 1.082 1_565 A
+C34 C71 1.407 . A
+C34 H36 1.079 1_565 A
+H35 C33 1.082 1_545 A
+H36 C34 1.079 1_545 A
+O37 Al51 1.798 . S
+O37 C55 1.292 . A
+O38 C54 1.291 . A
+O38 Al52 1.802 . S
+C39 C40 1.411 . A
+C39 C57 1.409 . A
+C39 H41 1.080 . S
+C40 C56 1.409 . A
+C40 H42 1.081 . S
+O43 Al52 1.803 . S
+O43 C60 1.296 . A
+O44 C59 1.294 . A
+O44 Al51 1.802 1_556 S
+C45 C46 1.411 . A
+C45 C62 1.407 . A
+C45 H47 1.081 . S
+C46 C61 1.408 . A
+C46 H48 1.080 . S
+Al49 O63 1.806 . S
+Al49 O1 1.799 1_545 A
+Al49 O8 1.799 1_554 A
+Al49 O25 1.803 1_454 S
+Al49 O32 1.803 1_445 S
+Al49 O53 1.809 1_544 A
+Al50 O58 1.809 . S
+Al50 O2 1.801 1_545 A
+Al50 O31 1.797 1_545 S
+Al50 O68 1.806 1_545 A
+Al51 O63 1.807 . S
+Al51 O13 1.804 1_454 A
+Al51 O20 1.799 1_455 A
+Al51 O44 1.802 1_554 S
+Al51 O53 1.808 1_554 A
+Al52 O68 1.808 . S
+Al52 O58 1.809 . S
+O53 H76 0.862 . S
+O53 Al49 1.809 1_566 A
+O53 Al51 1.808 1_556 A
+C54 C56 1.510 . S
+C55 C57 1.509 . S
+O58 H73 0.862 . S
+C59 C61 1.509 . S
+C60 C62 1.510 . S
+O63 H75 0.862 . S
+C64 C66 1.507 . S
+C65 C67 1.510 . S
+O68 H74 0.863 . S
+O68 Al50 1.806 1_565 A
+C69 C71 1.511 . S
+C70 C72 1.511 . S
diff --git a/benchmarks/mof/structures/CALF20.cif b/benchmarks/mof/structures/CALF20.cif
new file mode 100644
index 0000000000000000000000000000000000000000..a8cd85afe688a75219deeefab1c92e6649f848f5
--- /dev/null
+++ b/benchmarks/mof/structures/CALF20.cif
@@ -0,0 +1,79 @@
+data_CALF20
+_audit_creation_date 2025-01-03
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P21/C'
+_symmetry_Int_Tables_number 14
+_symmetry_cell_setting monoclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+ -x,y+1/2,-z+1/2
+ -x,-y,-z
+ x,-y+1/2,z+1/2
+_cell_length_a 8.9138
+_cell_length_b 9.6935
+_cell_length_c 9.4836
+_cell_angle_alpha 90.0000
+_cell_angle_beta 115.8950
+_cell_angle_gamma 90.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+Zn1 Zn 0.17588 0.05771 0.43679 0.01878 Uani 1.00
+N1 N 0.03080 -0.11080 0.36830 0.02235 Uani 1.00
+N2 N -0.09220 -0.14750 0.41000 0.02454 Uani 1.00
+N3 N -0.09920 -0.29140 0.22590 0.02563 Uani 1.00
+O1 O 0.40980 0.07610 0.61020 0.03235 Uani 1.00
+O2 O 0.67530 0.03070 0.67320 0.02972 Uani 1.00
+C1 C 0.02150 -0.19830 0.25880 0.02987 Uani 1.00
+H1A H 0.09320 -0.19550 0.20860 0.03600 Uiso 1.00
+C2 C -0.16550 -0.25540 0.32320 0.02964 Uani 1.00
+H2A H -0.25590 -0.30290 0.32890 0.03600 Uiso 1.00
+C3 C 0.52480 0.03080 0.58150 0.02323 Uani 1.00
+loop_
+_atom_site_aniso_label
+_atom_site_aniso_U_11
+_atom_site_aniso_U_22
+_atom_site_aniso_U_33
+_atom_site_aniso_U_12
+_atom_site_aniso_U_13
+_atom_site_aniso_U_23
+Zn1 0.01910 0.01960 0.02120 -0.00037 0.01210 0.00021
+N1 0.02510 0.02230 0.02770 -0.00420 0.01900 -0.00430
+N2 0.02610 0.02640 0.02940 -0.00570 0.01980 -0.00540
+N3 0.02870 0.02630 0.02900 -0.00580 0.01920 -0.00810
+O1 0.01990 0.04770 0.03200 0.00370 0.01370 -0.00870
+O2 0.02170 0.03940 0.02870 0.00180 0.01160 -0.00580
+C1 0.03180 0.03110 0.03860 -0.01080 0.02640 -0.01070
+C2 0.03200 0.03190 0.03800 -0.01230 0.02730 -0.01110
+C3 0.01980 0.02350 0.02720 0.00070 0.01100 0.00160
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_2
+_ccdc_geom_bond_type
+Zn1 N1 2.007 . S
+Zn1 O1 2.022 . S
+Zn1 N3 2.016 2 S
+Zn1 N2 2.091 3_556 S
+Zn1 O2 2.189 3_656 S
+N1 C1 1.315 . S
+N1 N2 1.365 . S
+N2 C2 1.315 . S
+N2 Zn1 2.091 3_556 S
+N3 C1 1.333 . S
+N3 C2 1.341 . S
+N3 Zn1 2.016 2_545 S
+O1 C3 1.250 . S
+O2 C3 1.240 . S
+O2 Zn1 2.189 3_656 S
+C1 H1A 0.950 . S
+C2 H2A 0.950 . S
+C3 C3 1.531 3_656 S
diff --git a/benchmarks/mof/structures/HKUST-1.cif b/benchmarks/mof/structures/HKUST-1.cif
new file mode 100644
index 0000000000000000000000000000000000000000..d7d2adad361d09830475837f67f22b7ca9a53f4d
--- /dev/null
+++ b/benchmarks/mof/structures/HKUST-1.cif
@@ -0,0 +1,180 @@
+data_FIQCEN_clean
+_audit_creation_date 2014-07-02
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 18.6273
+_cell_length_b 18.6273
+_cell_length_c 18.6273
+_cell_angle_alpha 60.0000
+_cell_angle_beta 60.0000
+_cell_angle_gamma 60.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+Cu1 Cu 0.57057 -0.00000 0.42943 0.01267 Uiso 1.00
+Cu2 Cu 0.00000 0.57057 0.00000 0.01267 Uiso 1.00
+Cu3 Cu 0.42943 0.00000 0.57057 0.01267 Uiso 1.00
+Cu4 Cu 0.00000 0.42943 0.00000 0.01267 Uiso 1.00
+Cu5 Cu 0.57057 0.42943 -0.00000 0.01267 Uiso 1.00
+Cu6 Cu 0.00000 1.00000 0.42943 0.01267 Uiso 1.00
+Cu7 Cu 0.42943 0.57057 -0.00000 0.01267 Uiso 1.00
+Cu8 Cu 0.00000 0.00000 0.57057 0.01267 Uiso 1.00
+Cu9 Cu 0.57057 1.00000 -0.00000 0.01267 Uiso 1.00
+Cu10 Cu 0.00000 0.42943 0.57057 0.01267 Uiso 1.00
+Cu11 Cu 0.42943 1.00000 0.00000 0.01267 Uiso 1.00
+Cu12 Cu 0.00000 0.57057 0.42943 0.01267 Uiso 1.00
+H1 H 0.72800 0.72800 0.51160 0.01267 Uiso 1.00
+H2 H 0.72800 0.72800 0.03240 0.01267 Uiso 1.00
+H3 H 0.51160 0.03240 0.72800 0.01267 Uiso 1.00
+H4 H 0.03240 0.51160 0.72800 0.01267 Uiso 1.00
+H5 H 0.72800 0.51160 0.03240 0.01267 Uiso 1.00
+H6 H 0.72800 0.03240 0.51160 0.01267 Uiso 1.00
+H7 H 0.51160 0.72800 0.72800 0.01267 Uiso 1.00
+H8 H 0.03240 0.72800 0.72800 0.01267 Uiso 1.00
+H9 H 0.72800 0.03240 0.72800 0.01267 Uiso 1.00
+H10 H 0.72800 0.51160 0.72800 0.01267 Uiso 1.00
+H11 H 0.51160 0.72800 0.03240 0.01267 Uiso 1.00
+H12 H 0.03240 0.72800 0.51160 0.01267 Uiso 1.00
+H13 H 0.27200 0.27200 0.48840 0.01267 Uiso 1.00
+H14 H 0.27200 0.27200 0.96760 0.01267 Uiso 1.00
+H15 H 0.96760 0.48840 0.27200 0.01267 Uiso 1.00
+H16 H 0.48840 0.96760 0.27200 0.01267 Uiso 1.00
+H17 H 0.48840 0.27200 0.96760 0.01267 Uiso 1.00
+H18 H 0.96760 0.27200 0.48840 0.01267 Uiso 1.00
+H19 H 0.27200 0.48840 0.27200 0.01267 Uiso 1.00
+H20 H 0.27200 0.96760 0.27200 0.01267 Uiso 1.00
+H21 H 0.96760 0.27200 0.27200 0.01267 Uiso 1.00
+H22 H 0.48840 0.27200 0.27200 0.01267 Uiso 1.00
+H23 H 0.27200 0.48840 0.96760 0.01267 Uiso 1.00
+H24 H 0.27200 0.96760 0.48840 0.01267 Uiso 1.00
+C1 C 0.25700 0.96900 0.38700 0.01267 Uiso 1.00
+C2 C 0.96900 0.25700 0.38700 0.01267 Uiso 1.00
+C3 C 0.38700 0.38700 0.25700 0.01267 Uiso 1.00
+C4 C 0.38700 0.38700 0.96900 0.01267 Uiso 1.00
+C5 C 0.25700 0.38700 0.38700 0.01267 Uiso 1.00
+C6 C 0.96900 0.38700 0.38700 0.01267 Uiso 1.00
+C7 C 0.38700 0.25700 0.96900 0.01267 Uiso 1.00
+C8 C 0.38700 0.96900 0.25700 0.01267 Uiso 1.00
+C9 C 0.25700 0.38700 0.96900 0.01267 Uiso 1.00
+C10 C 0.96900 0.38700 0.25700 0.01267 Uiso 1.00
+C11 C 0.38700 0.96900 0.38700 0.01267 Uiso 1.00
+C12 C 0.38700 0.25700 0.38700 0.01267 Uiso 1.00
+C13 C 0.03100 0.74300 0.61300 0.01267 Uiso 1.00
+C14 C 0.74300 0.03100 0.61300 0.01267 Uiso 1.00
+C15 C 0.61300 0.61300 0.74300 0.01267 Uiso 1.00
+C16 C 0.61300 0.61300 0.03100 0.01267 Uiso 1.00
+C17 C 0.61300 0.74300 0.61300 0.01267 Uiso 1.00
+C18 C 0.61300 0.03100 0.61300 0.01267 Uiso 1.00
+C19 C 0.74300 0.61300 0.03100 0.01267 Uiso 1.00
+C20 C 0.03100 0.61300 0.74300 0.01267 Uiso 1.00
+C21 C 0.61300 0.74300 0.03100 0.01267 Uiso 1.00
+C22 C 0.61300 0.03100 0.74300 0.01267 Uiso 1.00
+C23 C 0.03100 0.61300 0.61300 0.01267 Uiso 1.00
+C24 C 0.74300 0.61300 0.61300 0.01267 Uiso 1.00
+C25 C 0.56870 0.56870 0.83770 0.01267 Uiso 1.00
+C26 C 0.56870 0.56870 0.02490 0.01267 Uiso 1.00
+C27 C 0.83770 0.02490 0.56870 0.01267 Uiso 1.00
+C28 C 0.02490 0.83770 0.56870 0.01267 Uiso 1.00
+C29 C 0.56870 0.83770 0.02490 0.01267 Uiso 1.00
+C30 C 0.56870 0.02490 0.83770 0.01267 Uiso 1.00
+C31 C 0.83770 0.56870 0.56870 0.01267 Uiso 1.00
+C32 C 0.02490 0.56870 0.56870 0.01267 Uiso 1.00
+C33 C 0.56870 0.02490 0.56870 0.01267 Uiso 1.00
+C34 C 0.56870 0.83770 0.56870 0.01267 Uiso 1.00
+C35 C 0.83770 0.56870 0.02490 0.01267 Uiso 1.00
+C36 C 0.02490 0.56870 0.83770 0.01267 Uiso 1.00
+C37 C 0.43130 0.43130 0.16230 0.01267 Uiso 1.00
+C38 C 0.43130 0.43130 0.97510 0.01267 Uiso 1.00
+C39 C 0.97510 0.16230 0.43130 0.01267 Uiso 1.00
+C40 C 0.16230 0.97510 0.43130 0.01267 Uiso 1.00
+C41 C 0.16230 0.43130 0.97510 0.01267 Uiso 1.00
+C42 C 0.97510 0.43130 0.16230 0.01267 Uiso 1.00
+C43 C 0.43130 0.16230 0.43130 0.01267 Uiso 1.00
+C44 C 0.43130 0.97510 0.43130 0.01267 Uiso 1.00
+C45 C 0.97510 0.43130 0.43130 0.01267 Uiso 1.00
+C46 C 0.16230 0.43130 0.43130 0.01267 Uiso 1.00
+C47 C 0.43130 0.16230 0.97510 0.01267 Uiso 1.00
+C48 C 0.43130 0.97510 0.16230 0.01267 Uiso 1.00
+C49 C 0.30060 0.96840 0.43040 0.01267 Uiso 1.00
+C50 C 0.96840 0.30060 0.30060 0.01267 Uiso 1.00
+C51 C 0.43040 0.30060 0.30060 0.01267 Uiso 1.00
+C52 C 0.30060 0.43040 0.96840 0.01267 Uiso 1.00
+C53 C 0.30060 0.43040 0.30060 0.01267 Uiso 1.00
+C54 C 0.96840 0.30060 0.43040 0.01267 Uiso 1.00
+C55 C 0.43040 0.30060 0.96840 0.01267 Uiso 1.00
+C56 C 0.30060 0.96840 0.30060 0.01267 Uiso 1.00
+C57 C 0.30060 0.30060 0.96840 0.01267 Uiso 1.00
+C58 C 0.96840 0.43040 0.30060 0.01267 Uiso 1.00
+C59 C 0.43040 0.96840 0.30060 0.01267 Uiso 1.00
+C60 C 0.30060 0.30060 0.43040 0.01267 Uiso 1.00
+C61 C 0.03160 0.69940 0.56960 0.01267 Uiso 1.00
+C62 C 0.69940 0.03160 0.69940 0.01267 Uiso 1.00
+C63 C 0.69940 0.56960 0.69940 0.01267 Uiso 1.00
+C64 C 0.56960 0.69940 0.03160 0.01267 Uiso 1.00
+C65 C 0.56960 0.69940 0.69940 0.01267 Uiso 1.00
+C66 C 0.69940 0.03160 0.56960 0.01267 Uiso 1.00
+C67 C 0.69940 0.56960 0.03160 0.01267 Uiso 1.00
+C68 C 0.03160 0.69940 0.69940 0.01267 Uiso 1.00
+C69 C 0.69940 0.69940 0.03160 0.01267 Uiso 1.00
+C70 C 0.56960 0.03160 0.69940 0.01267 Uiso 1.00
+C71 C 0.03160 0.56960 0.69940 0.01267 Uiso 1.00
+C72 C 0.69940 0.69940 0.56960 0.01267 Uiso 1.00
+O1 O 0.61201 0.49247 0.87425 0.01267 Uiso 1.00
+O2 O 0.49247 0.61201 0.02127 0.01267 Uiso 1.00
+O3 O 0.87425 0.02127 0.61201 0.01267 Uiso 1.00
+O4 O 0.02127 0.87425 0.49247 0.01267 Uiso 1.00
+O5 O 0.61201 0.87425 0.02127 0.01267 Uiso 1.00
+O6 O 0.49247 0.02127 0.87425 0.01267 Uiso 1.00
+O7 O 0.87425 0.61201 0.49247 0.01267 Uiso 1.00
+O8 O 0.02127 0.49247 0.61201 0.01267 Uiso 1.00
+O9 O 0.61201 0.02127 0.49247 0.01267 Uiso 1.00
+O10 O 0.49247 0.87425 0.61201 0.01267 Uiso 1.00
+O11 O 0.87425 0.49247 0.02127 0.01267 Uiso 1.00
+O12 O 0.02127 0.61201 0.87425 0.01267 Uiso 1.00
+O13 O 0.50753 0.38799 0.12575 0.01267 Uiso 1.00
+O14 O 0.38799 0.50753 0.97873 0.01267 Uiso 1.00
+O15 O 0.97873 0.12575 0.38799 0.01267 Uiso 1.00
+O16 O 0.12575 0.97873 0.50753 0.01267 Uiso 1.00
+O17 O 0.12575 0.38799 0.97873 0.01267 Uiso 1.00
+O18 O 0.97873 0.50753 0.12575 0.01267 Uiso 1.00
+O19 O 0.38799 0.12575 0.50753 0.01267 Uiso 1.00
+O20 O 0.50753 0.97873 0.38799 0.01267 Uiso 1.00
+O21 O 0.97873 0.38799 0.50753 0.01267 Uiso 1.00
+O22 O 0.12575 0.50753 0.38799 0.01267 Uiso 1.00
+O23 O 0.50753 0.12575 0.97873 0.01267 Uiso 1.00
+O24 O 0.38799 0.97873 0.12575 0.01267 Uiso 1.00
+O25 O 0.38799 0.50753 0.12575 0.01267 Uiso 1.00
+O26 O 0.50753 0.38799 0.97873 0.01267 Uiso 1.00
+O27 O 0.12575 0.97873 0.38799 0.01267 Uiso 1.00
+O28 O 0.97873 0.12575 0.50753 0.01267 Uiso 1.00
+O29 O 0.38799 0.12575 0.97873 0.01267 Uiso 1.00
+O30 O 0.50753 0.97873 0.12575 0.01267 Uiso 1.00
+O31 O 0.12575 0.38799 0.50753 0.01267 Uiso 1.00
+O32 O 0.97873 0.50753 0.38799 0.01267 Uiso 1.00
+O33 O 0.38799 0.97873 0.50753 0.01267 Uiso 1.00
+O34 O 0.50753 0.12575 0.38799 0.01267 Uiso 1.00
+O35 O 0.12575 0.50753 0.97873 0.01267 Uiso 1.00
+O36 O 0.97873 0.38799 0.12575 0.01267 Uiso 1.00
+O37 O 0.49247 0.61201 0.87425 0.01267 Uiso 1.00
+O38 O 0.61201 0.49247 0.02127 0.01267 Uiso 1.00
+O39 O 0.02127 0.87425 0.61201 0.01267 Uiso 1.00
+O40 O 0.87425 0.02127 0.49247 0.01267 Uiso 1.00
+O41 O 0.87425 0.61201 0.02127 0.01267 Uiso 1.00
+O42 O 0.02127 0.49247 0.87425 0.01267 Uiso 1.00
+O43 O 0.61201 0.87425 0.49247 0.01267 Uiso 1.00
+O44 O 0.49247 0.02127 0.61201 0.01267 Uiso 1.00
+O45 O 0.02127 0.61201 0.49247 0.01267 Uiso 1.00
+O46 O 0.87425 0.49247 0.61201 0.01267 Uiso 1.00
+O47 O 0.49247 0.87425 0.02127 0.01267 Uiso 1.00
+O48 O 0.61201 0.02127 0.87425 0.01267 Uiso 1.00
diff --git a/benchmarks/mof/structures/MOF-5.cif b/benchmarks/mof/structures/MOF-5.cif
new file mode 100644
index 0000000000000000000000000000000000000000..f49b1517443dde7d85b82c5e5835d479cc451062
--- /dev/null
+++ b/benchmarks/mof/structures/MOF-5.cif
@@ -0,0 +1,130 @@
+data_EDUSIF_clean
+_audit_creation_date 2014-07-02
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 18.2660
+_cell_length_b 18.2660
+_cell_length_c 18.2660
+_cell_angle_alpha 60.0000
+_cell_angle_beta 60.0000
+_cell_angle_gamma 60.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+Zn1 Zn 0.29338 0.29338 0.29338 0.01267 Uiso 1.00
+Zn2 Zn 0.29338 0.29338 0.11986 0.01267 Uiso 1.00
+Zn3 Zn 0.29338 0.11986 0.29338 0.01267 Uiso 1.00
+Zn4 Zn 0.11986 0.29338 0.29338 0.01267 Uiso 1.00
+Zn5 Zn 0.70662 0.70662 0.70662 0.01267 Uiso 1.00
+Zn6 Zn 0.70662 0.70662 0.88014 0.01267 Uiso 1.00
+Zn7 Zn 0.88014 0.70662 0.70662 0.01267 Uiso 1.00
+Zn8 Zn 0.70662 0.88014 0.70662 0.01267 Uiso 1.00
+H1 H 0.15460 0.45520 0.45520 0.01267 Uiso 1.00
+H2 H 0.45520 0.15460 0.93500 0.01267 Uiso 1.00
+H3 H 0.45520 0.93500 0.15460 0.01267 Uiso 1.00
+H4 H 0.93500 0.45520 0.45520 0.01267 Uiso 1.00
+H5 H 0.15460 0.45520 0.93500 0.01267 Uiso 1.00
+H6 H 0.45520 0.93500 0.45520 0.01267 Uiso 1.00
+H7 H 0.45520 0.15460 0.45520 0.01267 Uiso 1.00
+H8 H 0.93500 0.45520 0.15460 0.01267 Uiso 1.00
+H9 H 0.15460 0.93500 0.45520 0.01267 Uiso 1.00
+H10 H 0.45520 0.45520 0.15460 0.01267 Uiso 1.00
+H11 H 0.45520 0.45520 0.93500 0.01267 Uiso 1.00
+H12 H 0.93500 0.15460 0.45520 0.01267 Uiso 1.00
+H13 H 0.54480 0.84540 0.54480 0.01267 Uiso 1.00
+H14 H 0.84540 0.54480 0.06500 0.01267 Uiso 1.00
+H15 H 0.06500 0.54480 0.84540 0.01267 Uiso 1.00
+H16 H 0.54480 0.06500 0.54480 0.01267 Uiso 1.00
+H17 H 0.54480 0.84540 0.06500 0.01267 Uiso 1.00
+H18 H 0.06500 0.54480 0.54480 0.01267 Uiso 1.00
+H19 H 0.84540 0.54480 0.54480 0.01267 Uiso 1.00
+H20 H 0.54480 0.06500 0.84540 0.01267 Uiso 1.00
+H21 H 0.06500 0.84540 0.54480 0.01267 Uiso 1.00
+H22 H 0.54480 0.54480 0.84540 0.01267 Uiso 1.00
+H23 H 0.54480 0.54480 0.06500 0.01267 Uiso 1.00
+H24 H 0.84540 0.06500 0.54480 0.01267 Uiso 1.00
+C1 C 0.09270 0.47310 0.47310 0.01267 Uiso 1.00
+C2 C 0.47310 0.09270 0.96110 0.01267 Uiso 1.00
+C3 C 0.47310 0.96110 0.09270 0.01267 Uiso 1.00
+C4 C 0.96110 0.47310 0.47310 0.01267 Uiso 1.00
+C5 C 0.09270 0.47310 0.96110 0.01267 Uiso 1.00
+C6 C 0.47310 0.96110 0.47310 0.01267 Uiso 1.00
+C7 C 0.47310 0.09270 0.47310 0.01267 Uiso 1.00
+C8 C 0.96110 0.47310 0.09270 0.01267 Uiso 1.00
+C9 C 0.09270 0.96110 0.47310 0.01267 Uiso 1.00
+C10 C 0.47310 0.47310 0.09270 0.01267 Uiso 1.00
+C11 C 0.47310 0.47310 0.96110 0.01267 Uiso 1.00
+C12 C 0.96110 0.09270 0.47310 0.01267 Uiso 1.00
+C13 C 0.52690 0.90730 0.52690 0.01267 Uiso 1.00
+C14 C 0.90730 0.52690 0.03890 0.01267 Uiso 1.00
+C15 C 0.03890 0.52690 0.90730 0.01267 Uiso 1.00
+C16 C 0.52690 0.03890 0.52690 0.01267 Uiso 1.00
+C17 C 0.52690 0.90730 0.03890 0.01267 Uiso 1.00
+C18 C 0.03890 0.52690 0.52690 0.01267 Uiso 1.00
+C19 C 0.90730 0.52690 0.52690 0.01267 Uiso 1.00
+C20 C 0.52690 0.03890 0.90730 0.01267 Uiso 1.00
+C21 C 0.03890 0.90730 0.52690 0.01267 Uiso 1.00
+C22 C 0.52690 0.52690 0.90730 0.01267 Uiso 1.00
+C23 C 0.52690 0.52690 0.03890 0.01267 Uiso 1.00
+C24 C 0.90730 0.03890 0.52690 0.01267 Uiso 1.00
+C25 C 0.11130 0.38870 0.38870 0.01267 Uiso 1.00
+C26 C 0.05380 0.44620 0.44620 0.01267 Uiso 1.00
+C27 C 0.38870 0.11130 0.11130 0.01267 Uiso 1.00
+C28 C 0.44620 0.05380 0.05380 0.01267 Uiso 1.00
+C29 C 0.11130 0.38870 0.11130 0.01267 Uiso 1.00
+C30 C 0.05380 0.44620 0.05380 0.01267 Uiso 1.00
+C31 C 0.38870 0.11130 0.38870 0.01267 Uiso 1.00
+C32 C 0.44620 0.05380 0.44620 0.01267 Uiso 1.00
+C33 C 0.11130 0.11130 0.38870 0.01267 Uiso 1.00
+C34 C 0.05380 0.05380 0.44620 0.01267 Uiso 1.00
+C35 C 0.38870 0.38870 0.11130 0.01267 Uiso 1.00
+C36 C 0.44620 0.44620 0.05380 0.01267 Uiso 1.00
+C37 C 0.61130 0.88870 0.61130 0.01267 Uiso 1.00
+C38 C 0.55380 0.94620 0.55380 0.01267 Uiso 1.00
+C39 C 0.88870 0.61130 0.88870 0.01267 Uiso 1.00
+C40 C 0.94620 0.55380 0.94620 0.01267 Uiso 1.00
+C41 C 0.61130 0.88870 0.88870 0.01267 Uiso 1.00
+C42 C 0.55380 0.94620 0.94620 0.01267 Uiso 1.00
+C43 C 0.88870 0.61130 0.61130 0.01267 Uiso 1.00
+C44 C 0.94620 0.55380 0.55380 0.01267 Uiso 1.00
+C45 C 0.88870 0.88870 0.61130 0.01267 Uiso 1.00
+C46 C 0.94620 0.94620 0.55380 0.01267 Uiso 1.00
+C47 C 0.61130 0.61130 0.88870 0.01267 Uiso 1.00
+C48 C 0.55380 0.55380 0.94620 0.01267 Uiso 1.00
+O1 O 0.25000 0.25000 0.25000 0.01267 Uiso 1.00
+O2 O 0.75000 0.75000 0.75000 0.01267 Uiso 1.00
+O3 O 0.19777 0.36600 0.36600 0.01267 Uiso 1.00
+O4 O 0.36600 0.19777 0.07023 0.01267 Uiso 1.00
+O5 O 0.36600 0.07023 0.19777 0.01267 Uiso 1.00
+O6 O 0.07023 0.36600 0.36600 0.01267 Uiso 1.00
+O7 O 0.19777 0.36600 0.07023 0.01267 Uiso 1.00
+O8 O 0.36600 0.07023 0.36600 0.01267 Uiso 1.00
+O9 O 0.36600 0.19777 0.36600 0.01267 Uiso 1.00
+O10 O 0.07023 0.36600 0.19777 0.01267 Uiso 1.00
+O11 O 0.19777 0.07023 0.36600 0.01267 Uiso 1.00
+O12 O 0.36600 0.36600 0.19777 0.01267 Uiso 1.00
+O13 O 0.36600 0.36600 0.07023 0.01267 Uiso 1.00
+O14 O 0.07023 0.19777 0.36600 0.01267 Uiso 1.00
+O15 O 0.63400 0.80223 0.63400 0.01267 Uiso 1.00
+O16 O 0.80223 0.63400 0.92977 0.01267 Uiso 1.00
+O17 O 0.92977 0.63400 0.80223 0.01267 Uiso 1.00
+O18 O 0.63400 0.92977 0.63400 0.01267 Uiso 1.00
+O19 O 0.63400 0.80223 0.92977 0.01267 Uiso 1.00
+O20 O 0.92977 0.63400 0.63400 0.01267 Uiso 1.00
+O21 O 0.80223 0.63400 0.63400 0.01267 Uiso 1.00
+O22 O 0.63400 0.92977 0.80223 0.01267 Uiso 1.00
+O23 O 0.92977 0.80223 0.63400 0.01267 Uiso 1.00
+O24 O 0.63400 0.63400 0.80223 0.01267 Uiso 1.00
+O25 O 0.63400 0.63400 0.92977 0.01267 Uiso 1.00
+O26 O 0.80223 0.92977 0.63400 0.01267 Uiso 1.00
diff --git a/benchmarks/mof/structures/SIFSIX-3-Cu.cif b/benchmarks/mof/structures/SIFSIX-3-Cu.cif
new file mode 100644
index 0000000000000000000000000000000000000000..a90ea36c24c944c89b8b1a16c7d9db3db6dec34f
--- /dev/null
+++ b/benchmarks/mof/structures/SIFSIX-3-Cu.cif
@@ -0,0 +1,468 @@
+ data_SIFIX-3-Cu_Mohamed_EDDAOUDI_FMD3_KAUST
+
+#=============================================================================
+
+# 1. SUBMISSION DETAILS
+
+_publ_contact_author_name 'Prof. Mohamed EDDAOUDI'
+_publ_contact_author_address
+;
+Functional Material Design, development & Discovery (FMD3), Advanced Membrane &
+Porous Materials (AMPM); King Abdullah University of Science and Technology
+(KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
+;
+_publ_contact_author_email ' Mohamed.eddaoudi@kaust.edu.sa '
+_publ_contact_author_fax ?
+_publ_contact_author_phone ?
+
+_publ_contact_letter
+; ?
+;
+
+_publ_requested_journal ' Nature Communications '
+_publ_requested_coeditor_name ?
+_publ_requested_category ? # Acta C: one of CI/CM/CO/FI/FM/FO
+
+
+# Definition of non standard CIF items (Reliability indices used in FULLPROF)
+
+loop_
+_publ_manuscript_incl_extra_item
+_publ_manuscript_incl_extra_info
+_publ_manuscript_incl_extra_defn
+# Name Explanation Standard?
+# ------ ----------- ---------
+ '_pd_proc_ls_prof_cR_factor' 'Prof. R-factor CORRECTED for background' no
+ '_pd_proc_ls_prof_cwR_factor' 'wProf.R-factor CORRECTED for background' no
+ '_pd_proc_ls_prof_cwR_expected' 'wProf.Expected CORRECTED for background' no
+ '_pd_proc_ls_prof_chi2' 'Chi-square for all considered points' no
+ '_pd_proc_ls_prof_echi2' 'Chi-2 for points with Bragg contribution' no
+#=============================================================================
+
+# 3. TITLE AND AUTHOR LIST
+
+_publ_section_title
+; ' SIFIX-3-Cu'
+;
+_publ_section_title_footnote
+;
+;
+
+# The loop structure below should contain the names and addresses of all
+# authors, in the required order of publication. Repeat as necessary.
+
+loop_
+ _publ_author_name
+ _publ_author_footnote
+ _publ_author_address
+? #<--'Last name, first name'
+; ?
+;
+; ?
+;
+
+#=============================================================================
+
+# 4. TEXT
+
+_publ_section_synopsis
+; ?
+;
+_publ_section_abstract
+; ?
+;
+_publ_section_comment
+; ?
+;
+_publ_section_exptl_prep # Details of the preparation of the sample(s)
+ # should be given here.
+; ?
+;
+_publ_section_exptl_refinement
+; ?
+;
+_publ_section_references
+; ?
+;
+_publ_section_figure_captions
+; ?
+;
+_publ_section_acknowledgements
+; ?
+;
+
+#=============================================================================
+
+#=============================================================================
+# If more than one structure is reported, the remaining sections should be
+# completed per structure. For each data set, replace the '?' in the
+# data_? line below by a unique identifier.
+
+data_SIFIX-3-Cu_Mohamed_EDDAOUDI_KAUST
+
+#=============================================================================
+
+# 5. CHEMICAL DATA
+
+_chemical_name_systematic
+; ?
+;
+_chemical_name_common ?
+_chemical_formula_moiety ' C8 H8 Cu F6 N4 Si '
+_chemical_formula_sum ' C8 H8 Cu F6 N4 Si '
+_chemical_formula_weight 365.82
+
+loop_
+ _atom_type_symbol
+ _atom_type_scat_Cromer_Mann_a1
+ _atom_type_scat_Cromer_Mann_b1
+ _atom_type_scat_Cromer_Mann_a2
+ _atom_type_scat_Cromer_Mann_b2
+ _atom_type_scat_Cromer_Mann_a3
+ _atom_type_scat_Cromer_Mann_b3
+ _atom_type_scat_Cromer_Mann_a4
+ _atom_type_scat_Cromer_Mann_b4
+ _atom_type_scat_Cromer_Mann_c
+ _atom_type_scat_dispersion_real
+ _atom_type_scat_dispersion_imag
+ _atom_type_scat_source
+n 12.21260 0.00570 3.13220 9.89330 2.01250 28.99750
+ 1.16630 0.58260 -11.52900 0.02900 0.01800
+ International_Tables_for_Crystallography_Vol.C(1991)_Tables_6.1.1.4_and_6.1.1.5
+c 2.31000 20.84390 1.02000 10.20750 1.58860 0.56870
+ 0.86500 51.65120 0.21560 0.01700 0.00900
+ International_Tables_for_Crystallography_Vol.C(1991)_Tables_6.1.1.4_and_6.1.1.5
+h 0.49300 10.51090 0.32291 26.12570 0.14019 3.14236
+ 0.04081 57.79970 0.00304 0.00000 0.00000
+ International_Tables_for_Crystallography_Vol.C(1991)_Tables_6.1.1.4_and_6.1.1.5
+cu 13.33800 3.58280 7.16760 0.24700 5.61580 11.39660
+ 1.67350 64.81260 1.19100 -2.01900 0.58900
+ International_Tables_for_Crystallography_Vol.C(1991)_Tables_6.1.1.4_and_6.1.1.5
+f 3.53920 10.28250 2.64120 4.29440 1.51700 0.26150
+ 1.02430 26.14760 0.27760 0.06900 0.05300
+ International_Tables_for_Crystallography_Vol.C(1991)_Tables_6.1.1.4_and_6.1.1.5
+si 6.29150 2.43860 3.03530 32.33370 1.98910 0.67850
+ 1.54100 81.69370 1.14070 0.24400 0.33000
+ International_Tables_for_Crystallography_Vol.C(1991)_Tables_6.1.1.4_and_6.1.1.5
+o 3.04850 13.27710 2.28680 5.70110 1.54630 0.32390
+ 0.86700 32.90890 0.25080 0.04700 0.03200
+ International_Tables_for_Crystallography_Vol.C(1991)_Tables_6.1.1.4_and_6.1.1.5
+
+#=============================================================================
+
+# 6. POWDER SPECIMEN AND CRYSTAL DATA
+
+_symmetry_cell_setting Tetragonal
+_symmetry_space_group_name_H-M 'P 4/m m m'
+_symmetry_space_group_name_Hall '-P 4 2'
+
+loop_
+ _symmetry_equiv_pos_as_xyz #<--must include 'x,y,z'
+'x,y,z'
+'-y,x,z'
+'-x,-y,z'
+'y,-x,z'
+'-x,y,z'
+'y,x,z'
+'x,-y,z'
+'-y,-x,z'
+'-x,-y,-z'
+'y,-x,-z'
+'x,y,-z'
+'-y,x,-z'
+'x,-y,-z'
+'-y,-x,-z'
+'-x,y,-z'
+'y,x,-z'
+
+_cell_length_a 6.9186(2)
+_cell_length_b 6.9186(2)
+_cell_length_c 7.9061(3)
+_cell_angle_alpha 90.00000
+_cell_angle_beta 90.00000
+_cell_angle_gamma 90.00000
+_cell_volume 378.44(2)
+_cell_formula_units_Z 1
+_cell_measurement_temperature 298
+_cell_special_details
+; ?
+;
+# The next three fields give the specimen dimensions in mm. The equatorial
+# plane contains the incident and diffracted beam.
+
+_pd_spec_size_axial ? # perpendicular to
+ # equatorial plane
+_pd_spec_size_equat ? # parallel to
+ # scattering vector
+ # in transmission
+_pd_spec_size_thick ? # parallel to
+ # scattering vector
+ # in reflection
+
+# The next five fields are character fields that describe the specimen.
+
+_pd_spec_mounting # This field should be
+ # used to give details of the
+ # container.
+; ?
+;
+_pd_spec_mount_mode ? # options are 'reflection'
+ # or 'transmission'
+_pd_spec_shape ? # options are 'cylinder'
+ # 'flat_sheet' or 'irregular'
+_pd_char_particle_morphology ?
+_pd_char_colour ? # use ICDD colour descriptions
+
+# The following three fields describe the preparation of the specimen.
+# The cooling rate is in K/min. The pressure at which the sample was
+# prepared is in kPa. The temperature of preparation is in K.
+
+_pd_prep_cool_rate ?
+_pd_prep_pressure ?
+_pd_prep_temperature ?
+
+# The next four fields are normally only needed for transmission experiments.
+
+_exptl_absorpt_coefficient_mu ?
+_exptl_absorpt_correction_type ?
+_exptl_absorpt_process_details ?
+_exptl_absorpt_correction_T_min ?
+_exptl_absorpt_correction_T_max ?
+
+#=============================================================================
+
+# 7. EXPERIMENTAL DATA
+
+_exptl_special_details
+; ?
+;
+
+# The following item is used to identify the equipment used to record
+# the powder pattern when the diffractogram was measured at a laboratory
+# other than the authors' home institution, e.g. when neutron or synchrotron
+# radiation is used.
+
+_pd_instr_location
+; ?
+;
+_pd_calibration_special_details # description of the method used
+ # to calibrate the instrument
+; ?
+;
+
+_diffrn_ambient_temperature 298
+_diffrn_source 'classical X-ray tube'
+_diffrn_radiation_type 'Cu K\a'
+_diffrn_radiation_wavelength 1.5418
+_diffrn_radiation_monochromator ?
+_diffrn_measurement_device_type ?
+_diffrn_measurement_method ?
+_diffrn_detector_area_resol_mean ? # Not in version 2.0.1
+_diffrn_detector ' X PANanalytical'
+_diffrn_detector_type ' CCD'
+_pd_meas_scan_method 'step-scan '
+_pd_meas_special_details
+; ?
+;
+
+# The following four items give details of the measured (not processed)
+# powder pattern. Angles are in degrees.
+
+_pd_meas_number_of_points 1501
+_pd_meas_2theta_range_min 3.00000
+_pd_meas_2theta_range_max 78.00000
+_pd_meas_2theta_range_inc 0.050000
+
+#=============================================================================
+
+# 8. REFINEMENT DATA
+
+_refine_special_details
+; ?
+;
+
+# Use the next field to give any special details about the fitting of the
+# powder pattern.
+
+_pd_proc_ls_special_details
+; ?
+;
+
+# The next three items are given as text.
+
+_pd_proc_ls_profile_function ?
+_pd_proc_ls_background_function ?
+_pd_proc_ls_pref_orient_corr
+; ?
+;
+
+# The following profile R-factors are NOT CORRECTED for background
+# The sum is extended to all non-excluded points.
+# These are the current CIF standard
+
+_pd_proc_ls_prof_R_factor 1.8180
+_pd_proc_ls_prof_wR_factor 2.7928
+_pd_proc_ls_prof_wR_expected 0.6200
+
+# The following profile R-factors are CORRECTED for background
+# The sum is extended to all non-excluded points.
+# These items are not in the current CIF standard, but are defined above
+
+_pd_proc_ls_prof_cR_factor 15.1079
+_pd_proc_ls_prof_cwR_factor 12.3179
+_pd_proc_ls_prof_cwR_expected 2.7348
+
+# The following items are not in the CIF standard, but are defined above
+
+_pd_proc_ls_prof_chi2 20.2879
+_pd_proc_ls_prof_echi2 20.7262
+
+# Items related to LS refinement
+
+_refine_ls_R_I_factor 5.1628
+_refine_ls_number_reflns 192
+_refine_ls_number_parameters 91
+_refine_ls_number_restraints 0
+_refine_ls_goodness_of_fit_all 20.7
+
+# The following four items apply to angular dispersive measurements.
+# 2theta minimum, maximum and increment (in degrees) are for the
+# intensities used in the refinement.
+
+_pd_proc_2theta_range_min 3.1108
+_pd_proc_2theta_range_max 78.1108
+_pd_proc_2theta_range_inc 0.050000
+_pd_proc_wavelength 1.540510
+
+_pd_block_diffractogram_id ? # The id used for the block containing
+ # the powder pattern profile (section 11)
+
+# Give appropriate details in the next two text fields.
+
+_pd_proc_info_excluded_regions ?
+_pd_proc_info_data_reduction ?
+
+# The following items are used to identify the programs used.
+
+_computing_cell_refinement 'McMaille (Le Bail, 2004)'
+_computing_structure_solution 'ESPOIR (Le Bail, 2001)'
+_computing_structure_refinement 'FULLPROF (Rodriguez-Carvajal, 1993)'
+_computing_molecular_graphics 'DIAMOND, '
+_computing_publication_material 'PLATON (Spek, 2003)'
+
+#=============================================================================
+
+# 9. ATOMIC COORDINATES AND DISPLACEMENT PARAMETERS
+
+loop_
+ _atom_site_label
+ _atom_site_fract_x
+ _atom_site_fract_y
+ _atom_site_fract_z
+ _atom_site_U_iso_or_equiv
+ _atom_site_occupancy
+ _atom_site_adp_type # Not in version 2.0.1
+ _atom_site_type_symbol
+ N1 0.50000 0.217(2) 0.50000 0.099(8) 1.00000 Uiso N
+ C1 0.50000 0.0893(1) 0.3843(1) 0.121(1) 1.00000 Uiso C
+ H1 0.50000 0.14010 0.27980 0.1665 1.00000 Uiso H
+ Cu1 0.50000 0.50000 0.50000 0.098(3) 1.00000 Uiso Cu
+ F1 0.50000 0.50000 0.232(3) 0.119(8) 1.00000 Uiso F
+ F2 0.3313(15) 0.3313(15) 0.00000 0.204(8) 1.00000 Uiso F
+ Si1 0.50000 0.50000 0.00000 0.158(7) 1.00000 Uiso Si
+
+
+# Note: if the displacement parameters were refined anisotropically
+# the U matrices should be given as for single-crystal studies.
+
+#=============================================================================
+
+## 10. DISTANCES AND ANGLES / MOLECULAR GEOMETRY
+
+_geom_special_details ?
+
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_1
+_geom_bond_site_symmetry_2
+_geom_bond_publ_flag
+ Cu1 F1 2.12(1) . . no
+ Cu1 N1 1.96(1) . . no
+ Si1 F1 1.83(1) . . no
+ Si1 F2 1.65(1) . . no
+ N1 C1 1.27(1) . . no
+ C1 C1 1.235(1) . 3_655 no
+ C1 H1 0.9000 . . no
+
+
+loop_
+_geom_angle_atom_site_label_1
+_geom_angle_atom_site_label_2
+_geom_angle_atom_site_label_3
+_geom_angle
+_geom_angle_site_symmetry_1
+_geom_angle_site_symmetry_2
+_geom_angle_site_symmetry_3
+_geom_angle_publ_flag
+ F1 Cu1 N1 90.00 . . . no
+ F1 Cu1 F1 180.00 . . . no
+ N1 Cu1 N1 90.00 . . 2_655 no
+ N1 Cu1 N1 180.00 . . 3_665 no
+ F1 Si1 F2 90.00 . . . no
+ F1 Si1 F1 180.00 . . . no
+ Cu1 F1 Si1 180.00 . . . no
+ Cu1 N1 C1 134.0(3) . . . no
+ C1 N1 C1 91.99 . . . no
+ N1 C1 H1 113.00 . . . no
+ C1 C1 H1 113.00 3_655 . . no
+
+
+
+loop_
+_geom_torsion_atom_site_label_1
+_geom_torsion_atom_site_label_2
+_geom_torsion_atom_site_label_3
+_geom_torsion_atom_site_label_4
+_geom_torsion_site_symmetry_1
+_geom_torsion_site_symmetry_2
+_geom_torsion_site_symmetry_3
+_geom_torsion_site_symmetry_4
+_geom_torsion
+_geom_torsion_publ_flag
+? ? ? ? ? ? ? ? ? ?
+
+loop_
+_geom_hbond_atom_site_label_D
+_geom_hbond_atom_site_label_H
+_geom_hbond_atom_site_label_A
+_geom_hbond_site_symmetry_D
+_geom_hbond_site_symmetry_H
+_geom_hbond_site_symmetry_A
+_geom_hbond_distance_DH
+_geom_hbond_distance_HA
+_geom_hbond_distance_DA
+_geom_hbond_angle_DHA
+_geom_hbond_publ_flag
+? ? ? ? ? ? ? ? ? ? ?
+
+#=============================================================================
+
+#=============================================================================
+# Additional structures (last six sections and associated data_? identifiers)
+# may be added at this point.
+#=============================================================================
+
+# The following lines are used to test the character set of files sent by
+# network email or other means. They are not part of the CIF data set.
+# abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789
+# !@#$%^&*()_+{}:"~<>?|\-=[];'`,./
+
+# start Validation Reply Form
+_vrf_PLAT601_SIFIX-3-Cu_Mohamed_EDDAOUDI_KAUST
+;
+PROBLEM: Structure Contains Solvent Accessible VOIDS of . 108 Ang3
+RESPONSE: Highly disordered water molecules are localized within channels.
+;
+# end Validation Reply Form
diff --git a/benchmarks/mof/structures/UiO-66.cif b/benchmarks/mof/structures/UiO-66.cif
new file mode 100644
index 0000000000000000000000000000000000000000..60010d7dc80d744953041eb301c315922eab83e4
--- /dev/null
+++ b/benchmarks/mof/structures/UiO-66.cif
@@ -0,0 +1,138 @@
+data_RUBTAK02_clean_h\(2)
+_audit_creation_date 2015-05-11
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 14.6675
+_cell_length_b 14.6675
+_cell_length_c 14.6675
+_cell_angle_alpha 60.0000
+_cell_angle_beta 60.0000
+_cell_angle_gamma 60.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+C1 C 0.91980 0.08020 0.44840 0.01267 Uiso 1.00
+O2 O 0.92340 0.07660 0.26860 0.01267 Uiso 1.00
+H3 H 0.85249 0.14751 0.40954 0.01267 Uiso 1.00
+C4 C 0.08020 0.91980 0.55160 0.01267 Uiso 1.00
+O5 O 0.07660 0.92340 0.73140 0.01267 Uiso 1.00
+H6 H 0.14751 0.85249 0.59046 0.01267 Uiso 1.00
+C7 C 0.44840 0.55160 0.91980 0.01267 Uiso 1.00
+O8 O 0.26860 0.73140 0.92340 0.01267 Uiso 1.00
+H9 H 0.40954 0.59046 0.85249 0.01267 Uiso 1.00
+C10 C 0.55160 0.44840 0.08020 0.01267 Uiso 1.00
+O11 O 0.73140 0.26860 0.07660 0.01267 Uiso 1.00
+H12 H 0.59046 0.40954 0.14751 0.01267 Uiso 1.00
+C13 C 0.44840 0.91980 0.08020 0.01267 Uiso 1.00
+O14 O 0.26860 0.92340 0.07660 0.01267 Uiso 1.00
+H15 H 0.40954 0.85249 0.14751 0.01267 Uiso 1.00
+C16 C 0.55160 0.08020 0.91980 0.01267 Uiso 1.00
+O17 O 0.73140 0.07660 0.92340 0.01267 Uiso 1.00
+H18 H 0.59046 0.14751 0.85249 0.01267 Uiso 1.00
+C19 C 0.91980 0.44840 0.55160 0.01267 Uiso 1.00
+O20 O 0.92340 0.26860 0.73140 0.01267 Uiso 1.00
+H21 H 0.85249 0.40954 0.59046 0.01267 Uiso 1.00
+C22 C 0.08020 0.55160 0.44840 0.01267 Uiso 1.00
+O23 O 0.07660 0.73140 0.26860 0.01267 Uiso 1.00
+H24 H 0.14751 0.59046 0.40954 0.01267 Uiso 1.00
+C25 C 0.08020 0.44840 0.91980 0.01267 Uiso 1.00
+O26 O 0.07660 0.26860 0.92340 0.01267 Uiso 1.00
+H27 H 0.14751 0.40954 0.85249 0.01267 Uiso 1.00
+C28 C 0.91980 0.55160 0.08020 0.01267 Uiso 1.00
+O29 O 0.92340 0.73140 0.07660 0.01267 Uiso 1.00
+H30 H 0.85249 0.59046 0.14751 0.01267 Uiso 1.00
+C31 C 0.55160 0.91980 0.44840 0.01267 Uiso 1.00
+O32 O 0.73140 0.92340 0.26860 0.01267 Uiso 1.00
+H33 H 0.59046 0.85249 0.40954 0.01267 Uiso 1.00
+C34 C 0.44840 0.08020 0.55160 0.01267 Uiso 1.00
+O35 O 0.26860 0.07660 0.73140 0.01267 Uiso 1.00
+H36 H 0.40954 0.14751 0.59046 0.01267 Uiso 1.00
+C37 C 0.08020 0.91980 0.44840 0.01267 Uiso 1.00
+O38 O 0.07660 0.92340 0.26860 0.01267 Uiso 1.00
+H39 H 0.14751 0.85249 0.40954 0.01267 Uiso 1.00
+C40 C 0.91980 0.08020 0.55160 0.01267 Uiso 1.00
+O41 O 0.92340 0.07660 0.73140 0.01267 Uiso 1.00
+H42 H 0.85249 0.14751 0.59046 0.01267 Uiso 1.00
+C43 C 0.55160 0.44840 0.91980 0.01267 Uiso 1.00
+O44 O 0.73140 0.26860 0.92340 0.01267 Uiso 1.00
+H45 H 0.59046 0.40954 0.85249 0.01267 Uiso 1.00
+C46 C 0.44840 0.55160 0.08020 0.01267 Uiso 1.00
+O47 O 0.26860 0.73140 0.07660 0.01267 Uiso 1.00
+H48 H 0.40954 0.59046 0.14751 0.01267 Uiso 1.00
+C49 C 0.91980 0.44840 0.08020 0.01267 Uiso 1.00
+O50 O 0.92340 0.26860 0.07660 0.01267 Uiso 1.00
+H51 H 0.85249 0.40954 0.14751 0.01267 Uiso 1.00
+C52 C 0.08020 0.55160 0.91980 0.01267 Uiso 1.00
+O53 O 0.07660 0.73140 0.92340 0.01267 Uiso 1.00
+H54 H 0.14751 0.59046 0.85249 0.01267 Uiso 1.00
+C55 C 0.44840 0.91980 0.55160 0.01267 Uiso 1.00
+O56 O 0.26860 0.92340 0.73140 0.01267 Uiso 1.00
+H57 H 0.40954 0.85249 0.59046 0.01267 Uiso 1.00
+C58 C 0.55160 0.08020 0.44840 0.01267 Uiso 1.00
+O59 O 0.73140 0.07660 0.26860 0.01267 Uiso 1.00
+H60 H 0.59046 0.14751 0.40954 0.01267 Uiso 1.00
+C61 C 0.44840 0.08020 0.91980 0.01267 Uiso 1.00
+O62 O 0.26860 0.07660 0.92340 0.01267 Uiso 1.00
+H63 H 0.40954 0.14751 0.85249 0.01267 Uiso 1.00
+C64 C 0.55160 0.91980 0.08020 0.01267 Uiso 1.00
+O65 O 0.73140 0.92340 0.07660 0.01267 Uiso 1.00
+H66 H 0.59046 0.85249 0.14751 0.01267 Uiso 1.00
+C67 C 0.91980 0.55160 0.44840 0.01267 Uiso 1.00
+O68 O 0.92340 0.73140 0.26860 0.01267 Uiso 1.00
+H69 H 0.85249 0.59046 0.40954 0.01267 Uiso 1.00
+C70 C 0.08020 0.44840 0.55160 0.01267 Uiso 1.00
+O71 O 0.07660 0.26860 0.73140 0.01267 Uiso 1.00
+H72 H 0.14751 0.40954 0.59046 0.01267 Uiso 1.00
+Zr73 Zr 0.87960 0.12040 0.12040 0.01267 Uiso 1.00
+Zr74 Zr 0.12040 0.87960 0.87960 0.01267 Uiso 1.00
+Zr75 Zr 0.12040 0.87960 0.12040 0.01267 Uiso 1.00
+Zr76 Zr 0.87960 0.12040 0.87960 0.01267 Uiso 1.00
+Zr77 Zr 0.12040 0.12040 0.87960 0.01267 Uiso 1.00
+Zr78 Zr 0.87960 0.87960 0.12040 0.01267 Uiso 1.00
+C79 C -0.00000 0.00000 0.29460 0.01267 Uiso 1.00
+C80 C -0.00000 0.00000 0.40380 0.01267 Uiso 1.00
+C81 C 0.00000 -0.00000 0.70540 0.01267 Uiso 1.00
+C82 C 0.00000 -0.00000 0.59620 0.01267 Uiso 1.00
+C83 C 0.29460 0.70540 -0.00000 0.01267 Uiso 1.00
+C84 C 0.40380 0.59620 -0.00000 0.01267 Uiso 1.00
+C85 C 0.70540 0.29460 0.00000 0.01267 Uiso 1.00
+C86 C 0.59620 0.40380 0.00000 0.01267 Uiso 1.00
+C87 C 0.29460 -0.00000 0.00000 0.01267 Uiso 1.00
+C88 C 0.40380 -0.00000 -0.00000 0.01267 Uiso 1.00
+C89 C 0.70540 0.00000 -0.00000 0.01267 Uiso 1.00
+C90 C 0.59620 0.00000 -0.00000 0.01267 Uiso 1.00
+C91 C -0.00000 0.29460 0.70540 0.01267 Uiso 1.00
+C92 C -0.00000 0.40380 0.59620 0.01267 Uiso 1.00
+C93 C -0.00000 0.70540 0.29460 0.01267 Uiso 1.00
+C94 C -0.00000 0.59620 0.40380 0.01267 Uiso 1.00
+C95 C -0.00000 0.29460 -0.00000 0.01267 Uiso 1.00
+C96 C -0.00000 0.40380 -0.00000 0.01267 Uiso 1.00
+C97 C -0.00000 0.70540 -0.00000 0.01267 Uiso 1.00
+C98 C -0.00000 0.59620 -0.00000 0.01267 Uiso 1.00
+C99 C 0.70540 -0.00000 0.29460 0.01267 Uiso 1.00
+C100 C 0.59620 -0.00000 0.40380 0.01267 Uiso 1.00
+C101 C 0.29460 -0.00000 0.70540 0.01267 Uiso 1.00
+C102 C 0.40380 -0.00000 0.59620 0.01267 Uiso 1.00
+O103 O 0.94390 0.16830 0.94390 0.01267 Uiso 1.00
+O104 O 0.05610 0.83170 0.05610 0.01267 Uiso 1.00
+O105 O 0.16830 0.94390 0.94390 0.01267 Uiso 1.00
+O106 O 0.83170 0.05610 0.05610 0.01267 Uiso 1.00
+O107 O 0.94390 0.94390 0.94390 0.01267 Uiso 1.00
+O108 O 0.05610 0.05610 0.05610 0.01267 Uiso 1.00
+O109 O 0.94390 0.94390 0.16830 0.01267 Uiso 1.00
+O110 O 0.05610 0.05610 0.83170 0.01267 Uiso 1.00
+H111 H 0.09200 0.09200 0.09200 0.00000 Uiso 1.00
+H112 H 0.09200 0.09200 0.72400 0.00000 Uiso 1.00
+H113 H 0.09200 0.72400 0.09200 0.00000 Uiso 1.00
+H114 H 0.72400 0.09200 0.09200 0.00000 Uiso 1.00
diff --git a/benchmarks/mof/structures/ZIF-7.cif b/benchmarks/mof/structures/ZIF-7.cif
new file mode 100644
index 0000000000000000000000000000000000000000..3a5cdd04e9841307919e7afc8a78814db019cd26
--- /dev/null
+++ b/benchmarks/mof/structures/ZIF-7.cif
@@ -0,0 +1,546 @@
+data_image0
+_cell_length_a 22.989
+_cell_length_b 22.989
+_cell_length_c 15.763
+_cell_angle_alpha 90
+_cell_angle_beta 90
+_cell_angle_gamma 120
+
+_symmetry_space_group_name_H-M "P 1"
+_symmetry_int_tables_number 1
+
+loop_
+ _symmetry_equiv_pos_as_xyz
+ 'x, y, z'
+
+loop_
+ _atom_site_label
+ _atom_site_occupancy
+ _atom_site_fract_x
+ _atom_site_fract_y
+ _atom_site_fract_z
+ _atom_site_thermal_displace_type
+ _atom_site_B_iso_or_equiv
+ _atom_site_type_symbol
+ C1 1.0000 0.69950 0.02850 0.17630 Biso 1.000 C
+ H1 1.0000 0.73820 0.07030 0.17200 Biso 1.000 H
+ C2 1.0000 0.60880 0.94520 0.22640 Biso 1.000 C
+ C3 1.0000 0.55290 0.90140 0.27220 Biso 1.000 C
+ H2 1.0000 0.54360 0.91310 0.32480 Biso 1.000 H
+ C4 1.0000 0.51170 0.83960 0.23700 Biso 1.000 C
+ H3 1.0000 0.47380 0.80870 0.26670 Biso 1.000 H
+ C5 1.0000 0.52510 0.82150 0.15630 Biso 1.000 C
+ H4 1.0000 0.49600 0.77900 0.13450 Biso 1.000 H
+ C6 1.0000 0.62220 0.92740 0.14630 Biso 1.000 C
+ C7 1.0000 0.58000 0.86540 0.11010 Biso 1.000 C
+ H5 1.0000 0.58860 0.85410 0.05670 Biso 1.000 H
+ C8 1.0000 0.80820 0.20120 0.33980 Biso 1.000 C
+ H6 1.0000 0.82440 0.18970 0.38710 Biso 1.000 H
+ C9 1.0000 0.80340 0.25700 0.23610 Biso 1.000 C
+ C10 1.0000 0.81400 0.30390 0.17010 Biso 1.000 C
+ H7 1.0000 0.85440 0.34400 0.16580 Biso 1.000 H
+ C11 1.0000 0.74430 0.19720 0.24000 Biso 1.000 C
+ C12 1.0000 0.69050 0.17970 0.18020 Biso 1.000 C
+ H8 1.0000 0.65050 0.13890 0.18320 Biso 1.000 H
+ C13 1.0000 0.76370 0.28860 0.11380 Biso 1.000 C
+ H9 1.0000 0.77020 0.31940 0.07140 Biso 1.000 H
+ C14 1.0000 0.70170 0.22710 0.11750 Biso 1.000 C
+ H10 1.0000 0.66830 0.21840 0.07800 Biso 1.000 H
+ N1 1.0000 0.65890 0.01130 0.24330 Biso 1.000 N
+ N2 1.0000 0.74800 0.16040 0.30920 Biso 1.000 N
+ N3 1.0000 0.68120 0.98190 0.11520 Biso 1.000 N
+ N4 1.0000 0.84450 0.25900 0.30060 Biso 1.000 N
+ Zn1 1.0000 0.67190 0.07047 0.34160 Biso 1.000 Zn
+ C15 1.0000 0.36617 0.36183 0.50963 Biso 1.000 C
+ H11 1.0000 0.40487 0.40363 0.50533 Biso 1.000 H
+ C16 1.0000 0.27547 0.27853 0.55973 Biso 1.000 C
+ C17 1.0000 0.21957 0.23473 0.60553 Biso 1.000 C
+ H12 1.0000 0.21027 0.24643 0.65813 Biso 1.000 H
+ C18 1.0000 0.17837 0.17293 0.57033 Biso 1.000 C
+ H13 1.0000 0.14047 0.14203 0.60003 Biso 1.000 H
+ C19 1.0000 0.19177 0.15483 0.48963 Biso 1.000 C
+ H14 1.0000 0.16267 0.11233 0.46783 Biso 1.000 H
+ C20 1.0000 0.28887 0.26073 0.47963 Biso 1.000 C
+ C21 1.0000 0.24667 0.19873 0.44343 Biso 1.000 C
+ H15 1.0000 0.25527 0.18743 0.39003 Biso 1.000 H
+ C22 1.0000 0.47487 0.53453 0.67313 Biso 1.000 C
+ H16 1.0000 0.49107 0.52303 0.72043 Biso 1.000 H
+ C23 1.0000 0.47007 0.59033 0.56943 Biso 1.000 C
+ C24 1.0000 0.48067 0.63723 0.50343 Biso 1.000 C
+ H17 1.0000 0.52107 0.67733 0.49913 Biso 1.000 H
+ C25 1.0000 0.41097 0.53053 0.57333 Biso 1.000 C
+ C26 1.0000 0.35717 0.51303 0.51353 Biso 1.000 C
+ H18 1.0000 0.31717 0.47223 0.51653 Biso 1.000 H
+ C27 1.0000 0.43037 0.62193 0.44713 Biso 1.000 C
+ H19 1.0000 0.43687 0.65273 0.40473 Biso 1.000 H
+ C28 1.0000 0.36837 0.56043 0.45083 Biso 1.000 C
+ H20 1.0000 0.33497 0.55173 0.41133 Biso 1.000 H
+ N5 1.0000 0.32557 0.34463 0.57663 Biso 1.000 N
+ N6 1.0000 0.41467 0.49373 0.64253 Biso 1.000 N
+ N7 1.0000 0.34787 0.31523 0.44853 Biso 1.000 N
+ N8 1.0000 0.51117 0.59233 0.63393 Biso 1.000 N
+ Zn2 1.0000 0.33857 0.40380 0.67493 Biso 1.000 Zn
+ C29 1.0000 0.03283 0.69517 0.84297 Biso 1.000 C
+ H21 1.0000 0.07153 0.73697 0.83867 Biso 1.000 H
+ C30 1.0000 0.94213 0.61187 0.89307 Biso 1.000 C
+ C31 1.0000 0.88623 0.56807 0.93887 Biso 1.000 C
+ H22 1.0000 0.87693 0.57977 0.99147 Biso 1.000 H
+ C32 1.0000 0.84503 0.50627 0.90367 Biso 1.000 C
+ H23 1.0000 0.80713 0.47537 0.93337 Biso 1.000 H
+ C33 1.0000 0.85843 0.48817 0.82297 Biso 1.000 C
+ H24 1.0000 0.82933 0.44567 0.80117 Biso 1.000 H
+ C34 1.0000 0.95553 0.59407 0.81297 Biso 1.000 C
+ C35 1.0000 0.91333 0.53207 0.77677 Biso 1.000 C
+ H25 1.0000 0.92193 0.52077 0.72337 Biso 1.000 H
+ C36 1.0000 0.14153 0.86787 0.00647 Biso 1.000 C
+ H26 1.0000 0.15773 0.85637 0.05377 Biso 1.000 H
+ C37 1.0000 0.13673 0.92367 0.90277 Biso 1.000 C
+ C38 1.0000 0.14733 0.97057 0.83677 Biso 1.000 C
+ H27 1.0000 0.18773 0.01067 0.83247 Biso 1.000 H
+ C39 1.0000 0.07763 0.86387 0.90667 Biso 1.000 C
+ C40 1.0000 0.02383 0.84637 0.84687 Biso 1.000 C
+ H28 1.0000 0.98383 0.80557 0.84987 Biso 1.000 H
+ C41 1.0000 0.09703 0.95527 0.78047 Biso 1.000 C
+ H29 1.0000 0.10353 0.98607 0.73807 Biso 1.000 H
+ C42 1.0000 0.03503 0.89377 0.78417 Biso 1.000 C
+ H30 1.0000 0.00163 0.88507 0.74467 Biso 1.000 H
+ N9 1.0000 0.99223 0.67797 0.90997 Biso 1.000 N
+ N10 1.0000 0.08133 0.82707 0.97587 Biso 1.000 N
+ N11 1.0000 0.01453 0.64857 0.78187 Biso 1.000 N
+ N12 1.0000 0.17783 0.92567 0.96727 Biso 1.000 N
+ Zn3 1.0000 0.00523 0.73714 0.00827 Biso 1.000 Zn
+ C43 1.0000 0.97150 0.67100 0.17630 Biso 1.000 C
+ H31 1.0000 0.92970 0.66790 0.17200 Biso 1.000 H
+ C44 1.0000 0.05480 0.66360 0.22640 Biso 1.000 C
+ C45 1.0000 0.09860 0.65150 0.27220 Biso 1.000 C
+ H32 1.0000 0.08690 0.63050 0.32480 Biso 1.000 H
+ C46 1.0000 0.16040 0.67210 0.23700 Biso 1.000 C
+ H33 1.0000 0.19130 0.66510 0.26670 Biso 1.000 H
+ C47 1.0000 0.17850 0.70360 0.15630 Biso 1.000 C
+ H34 1.0000 0.22100 0.71700 0.13450 Biso 1.000 H
+ C48 1.0000 0.07260 0.69480 0.14630 Biso 1.000 C
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+ N44 1.0000 0.82217 0.07433 0.03273 Biso 1.000 N
+ Zn11 1.0000 0.99477 0.26286 0.99173 Biso 1.000 Zn
+ C155 1.0000 0.63383 0.63817 0.49037 Biso 1.000 C
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+ C182 1.0000 0.22710 0.52540 0.88250 Biso 1.000 C
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+ N49 1.0000 0.01130 0.35240 0.75670 Biso 1.000 N
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+ N51 1.0000 0.98190 0.30070 0.88480 Biso 1.000 N
+ N52 1.0000 0.25900 0.41450 0.69940 Biso 1.000 N
+ Zn13 1.0000 0.07047 0.39857 0.65840 Biso 1.000 Zn
+ C183 1.0000 0.69517 0.66233 0.15703 Biso 1.000 C
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+ C184 1.0000 0.61187 0.66973 0.10693 Biso 1.000 C
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+ C188 1.0000 0.59407 0.63853 0.18703 Biso 1.000 C
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+ H135 1.0000 0.52077 0.59883 0.27663 Biso 1.000 H
+ C190 1.0000 0.86787 0.72633 0.99353 Biso 1.000 C
+ H136 1.0000 0.85637 0.69863 0.94623 Biso 1.000 H
+ C191 1.0000 0.92367 0.78693 0.09723 Biso 1.000 C
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+ H137 1.0000 0.01067 0.82293 0.16753 Biso 1.000 H
+ C193 1.0000 0.86387 0.78623 0.09333 Biso 1.000 C
+ C194 1.0000 0.84637 0.82253 0.15313 Biso 1.000 C
+ H138 1.0000 0.80557 0.82173 0.15013 Biso 1.000 H
+ C195 1.0000 0.95527 0.85823 0.21953 Biso 1.000 C
+ H139 1.0000 0.98607 0.88253 0.26193 Biso 1.000 H
+ C196 1.0000 0.89377 0.85873 0.21583 Biso 1.000 C
+ H140 1.0000 0.88507 0.88343 0.25533 Biso 1.000 H
+ N53 1.0000 0.67797 0.68573 0.09003 Biso 1.000 N
+ N54 1.0000 0.82707 0.74573 0.02413 Biso 1.000 N
+ N55 1.0000 0.64857 0.63403 0.21813 Biso 1.000 N
+ N56 1.0000 0.92567 0.74783 0.03273 Biso 1.000 N
+ Zn14 1.0000 0.73714 0.73190 0.99173 Biso 1.000 Zn
+ C197 1.0000 0.36183 0.99567 0.49037 Biso 1.000 C
+ H141 1.0000 0.40363 0.99877 0.49467 Biso 1.000 H
+ C198 1.0000 0.27853 0.00307 0.44027 Biso 1.000 C
+ C199 1.0000 0.23473 0.01517 0.39447 Biso 1.000 C
+ H142 1.0000 0.24643 0.03617 0.34187 Biso 1.000 H
+ C200 1.0000 0.17293 0.99457 0.42967 Biso 1.000 C
+ H143 1.0000 0.14203 0.00157 0.39997 Biso 1.000 H
+ C201 1.0000 0.15483 0.96307 0.51037 Biso 1.000 C
+ H144 1.0000 0.11233 0.94967 0.53217 Biso 1.000 H
+ C202 1.0000 0.26073 0.97187 0.52037 Biso 1.000 C
+ C203 1.0000 0.19873 0.95207 0.55657 Biso 1.000 C
+ H145 1.0000 0.18743 0.93217 0.60997 Biso 1.000 H
+ C204 1.0000 0.53453 0.05967 0.32687 Biso 1.000 C
+ H146 1.0000 0.52303 0.03197 0.27957 Biso 1.000 H
+ C205 1.0000 0.59033 0.12027 0.43057 Biso 1.000 C
+ C206 1.0000 0.63723 0.15657 0.49657 Biso 1.000 C
+ H147 1.0000 0.67733 0.15627 0.50087 Biso 1.000 H
+ C207 1.0000 0.53053 0.11957 0.42667 Biso 1.000 C
+ C208 1.0000 0.51303 0.15587 0.48647 Biso 1.000 C
+ H148 1.0000 0.47223 0.15507 0.48347 Biso 1.000 H
+ C209 1.0000 0.62193 0.19157 0.55287 Biso 1.000 C
+ H149 1.0000 0.65273 0.21587 0.59527 Biso 1.000 H
+ C210 1.0000 0.56043 0.19207 0.54917 Biso 1.000 C
+ H150 1.0000 0.55173 0.21677 0.58867 Biso 1.000 H
+ N57 1.0000 0.34463 0.01907 0.42337 Biso 1.000 N
+ N58 1.0000 0.49373 0.07907 0.35747 Biso 1.000 N
+ N59 1.0000 0.31523 0.96737 0.55147 Biso 1.000 N
+ N60 1.0000 0.59233 0.08117 0.36607 Biso 1.000 N
+ Zn15 1.0000 0.40380 0.06524 0.32507 Biso 1.000 Zn
+ C211 1.0000 0.67100 0.69950 0.82370 Biso 1.000 C
+ H151 1.0000 0.66790 0.73820 0.82800 Biso 1.000 H
+ C212 1.0000 0.66360 0.60880 0.77360 Biso 1.000 C
+ C213 1.0000 0.65150 0.55290 0.72780 Biso 1.000 C
+ H152 1.0000 0.63050 0.54360 0.67520 Biso 1.000 H
+ C214 1.0000 0.67210 0.51170 0.76300 Biso 1.000 C
+ H153 1.0000 0.66510 0.47380 0.73330 Biso 1.000 H
+ C215 1.0000 0.70360 0.52510 0.84370 Biso 1.000 C
+ H154 1.0000 0.71700 0.49600 0.86550 Biso 1.000 H
+ C216 1.0000 0.69480 0.62220 0.85370 Biso 1.000 C
+ C217 1.0000 0.71460 0.58000 0.88990 Biso 1.000 C
+ H155 1.0000 0.73450 0.58860 0.94330 Biso 1.000 H
+ C218 1.0000 0.60700 0.80820 0.66020 Biso 1.000 C
+ H156 1.0000 0.63470 0.82440 0.61290 Biso 1.000 H
+ C219 1.0000 0.54640 0.80340 0.76390 Biso 1.000 C
+ C220 1.0000 0.51010 0.81400 0.82990 Biso 1.000 C
+ H157 1.0000 0.51040 0.85440 0.83420 Biso 1.000 H
+ C221 1.0000 0.54710 0.74430 0.76000 Biso 1.000 C
+ C222 1.0000 0.51080 0.69050 0.81980 Biso 1.000 C
+ H158 1.0000 0.51160 0.65050 0.81680 Biso 1.000 H
+ C223 1.0000 0.47510 0.76370 0.88620 Biso 1.000 C
+ H159 1.0000 0.45080 0.77020 0.92860 Biso 1.000 H
+ C224 1.0000 0.47460 0.70170 0.88250 Biso 1.000 C
+ H160 1.0000 0.44990 0.66830 0.92200 Biso 1.000 H
+ N61 1.0000 0.64760 0.65890 0.75670 Biso 1.000 N
+ N62 1.0000 0.58760 0.74800 0.69080 Biso 1.000 N
+ N63 1.0000 0.69930 0.68120 0.88480 Biso 1.000 N
+ N64 1.0000 0.58550 0.84450 0.69940 Biso 1.000 N
+ Zn16 1.0000 0.60143 0.67190 0.65840 Biso 1.000 Zn
+ C225 1.0000 0.33767 0.03283 0.15703 Biso 1.000 C
+ H161 1.0000 0.33457 0.07153 0.16133 Biso 1.000 H
+ C226 1.0000 0.33027 0.94213 0.10693 Biso 1.000 C
+ C227 1.0000 0.31817 0.88623 0.06113 Biso 1.000 C
+ H162 1.0000 0.29717 0.87693 0.00853 Biso 1.000 H
+ C228 1.0000 0.33877 0.84503 0.09633 Biso 1.000 C
+ H163 1.0000 0.33177 0.80713 0.06663 Biso 1.000 H
+ C229 1.0000 0.37027 0.85843 0.17703 Biso 1.000 C
+ H164 1.0000 0.38367 0.82933 0.19883 Biso 1.000 H
+ C230 1.0000 0.36147 0.95553 0.18703 Biso 1.000 C
+ C231 1.0000 0.38127 0.91333 0.22323 Biso 1.000 C
+ H165 1.0000 0.40117 0.92193 0.27663 Biso 1.000 H
+ C232 1.0000 0.27367 0.14153 0.99353 Biso 1.000 C
+ H166 1.0000 0.30137 0.15773 0.94623 Biso 1.000 H
+ C233 1.0000 0.21307 0.13673 0.09723 Biso 1.000 C
+ C234 1.0000 0.17677 0.14733 0.16323 Biso 1.000 C
+ H167 1.0000 0.17707 0.18773 0.16753 Biso 1.000 H
+ C235 1.0000 0.21377 0.07763 0.09333 Biso 1.000 C
+ C236 1.0000 0.17747 0.02383 0.15313 Biso 1.000 C
+ H168 1.0000 0.17827 0.98383 0.15013 Biso 1.000 H
+ C237 1.0000 0.14177 0.09703 0.21953 Biso 1.000 C
+ H169 1.0000 0.11747 0.10353 0.26193 Biso 1.000 H
+ C238 1.0000 0.14127 0.03503 0.21583 Biso 1.000 C
+ H170 1.0000 0.11657 0.00163 0.25533 Biso 1.000 H
+ N65 1.0000 0.31427 0.99223 0.09003 Biso 1.000 N
+ N66 1.0000 0.25427 0.08133 0.02413 Biso 1.000 N
+ N67 1.0000 0.36597 0.01453 0.21813 Biso 1.000 N
+ N68 1.0000 0.25217 0.17783 0.03273 Biso 1.000 N
+ Zn17 1.0000 0.26810 0.00523 0.99173 Biso 1.000 Zn
+ C239 1.0000 0.00433 0.36617 0.49037 Biso 1.000 C
+ H171 1.0000 0.00123 0.40487 0.49467 Biso 1.000 H
+ C240 1.0000 0.99693 0.27547 0.44027 Biso 1.000 C
+ C241 1.0000 0.98483 0.21957 0.39447 Biso 1.000 C
+ H172 1.0000 0.96383 0.21027 0.34187 Biso 1.000 H
+ C242 1.0000 0.00543 0.17837 0.42967 Biso 1.000 C
+ H173 1.0000 0.99843 0.14047 0.39997 Biso 1.000 H
+ C243 1.0000 0.03693 0.19177 0.51037 Biso 1.000 C
+ H174 1.0000 0.05033 0.16267 0.53217 Biso 1.000 H
+ C244 1.0000 0.02813 0.28887 0.52037 Biso 1.000 C
+ C245 1.0000 0.04793 0.24667 0.55657 Biso 1.000 C
+ H175 1.0000 0.06783 0.25527 0.60997 Biso 1.000 H
+ C246 1.0000 0.94033 0.47487 0.32687 Biso 1.000 C
+ H176 1.0000 0.96803 0.49107 0.27957 Biso 1.000 H
+ C247 1.0000 0.87973 0.47007 0.43057 Biso 1.000 C
+ C248 1.0000 0.84343 0.48067 0.49657 Biso 1.000 C
+ H177 1.0000 0.84373 0.52107 0.50087 Biso 1.000 H
+ C249 1.0000 0.88043 0.41097 0.42667 Biso 1.000 C
+ C250 1.0000 0.84413 0.35717 0.48647 Biso 1.000 C
+ H178 1.0000 0.84493 0.31717 0.48347 Biso 1.000 H
+ C251 1.0000 0.80843 0.43037 0.55287 Biso 1.000 C
+ H179 1.0000 0.78413 0.43687 0.59527 Biso 1.000 H
+ C252 1.0000 0.80793 0.36837 0.54917 Biso 1.000 C
+ H180 1.0000 0.78323 0.33497 0.58867 Biso 1.000 H
+ N69 1.0000 0.98093 0.32557 0.42337 Biso 1.000 N
+ N70 1.0000 0.92093 0.41467 0.35747 Biso 1.000 N
+ N71 1.0000 0.03263 0.34787 0.55147 Biso 1.000 N
+ N72 1.0000 0.91883 0.51117 0.36607 Biso 1.000 N
+ Zn18 1.0000 0.93476 0.33857 0.32507 Biso 1.000 Zn
diff --git a/benchmarks/mof/structures/ZIF-8.cif b/benchmarks/mof/structures/ZIF-8.cif
new file mode 100644
index 0000000000000000000000000000000000000000..40827d68c164e2d0a42d3bab95593837ad0d53fc
--- /dev/null
+++ b/benchmarks/mof/structures/ZIF-8.cif
@@ -0,0 +1,162 @@
+data_OFERUN_clean
+_audit_creation_date 2014-07-02
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 14.7138
+_cell_length_b 14.7138
+_cell_length_c 14.7138
+_cell_angle_alpha 109.4710
+_cell_angle_beta 109.4710
+_cell_angle_gamma 109.4710
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+Zn1 Zn 0.25000 0.75000 0.50000 0.01267 Uiso 1.00
+Zn2 Zn 0.75000 0.25000 0.50000 0.01267 Uiso 1.00
+Zn3 Zn 0.50000 0.75000 0.25000 0.01267 Uiso 1.00
+Zn4 Zn 0.50000 0.25000 0.75000 0.01267 Uiso 1.00
+Zn5 Zn 0.75000 0.50000 0.25000 0.01267 Uiso 1.00
+Zn6 Zn 0.25000 0.50000 0.75000 0.01267 Uiso 1.00
+H1 H 0.42240 0.65640 0.53660 0.01267 Uiso 1.00
+H2 H 0.31040 0.79410 0.24430 0.01267 Uiso 1.00
+H3 H 0.11980 0.88580 0.46340 0.01267 Uiso 1.00
+H4 H 0.54980 0.06610 0.75570 0.01267 Uiso 1.00
+H5 H 0.88020 0.34360 0.76600 0.01267 Uiso 1.00
+H6 H 0.45020 0.20590 0.51630 0.01267 Uiso 1.00
+H7 H 0.57760 0.11420 0.23400 0.01267 Uiso 1.00
+H8 H 0.68960 0.93390 0.48370 0.01267 Uiso 1.00
+H9 H 0.76600 0.88020 0.34360 0.01267 Uiso 1.00
+H10 H 0.51630 0.45020 0.20590 0.01267 Uiso 1.00
+H11 H 0.23400 0.57760 0.11420 0.01267 Uiso 1.00
+H12 H 0.48370 0.68960 0.93390 0.01267 Uiso 1.00
+H13 H 0.53660 0.42240 0.65640 0.01267 Uiso 1.00
+H14 H 0.24430 0.31040 0.79410 0.01267 Uiso 1.00
+H15 H 0.46340 0.11980 0.88580 0.01267 Uiso 1.00
+H16 H 0.75570 0.54980 0.06610 0.01267 Uiso 1.00
+H17 H 0.88580 0.46340 0.11980 0.01267 Uiso 1.00
+H18 H 0.06610 0.75570 0.54980 0.01267 Uiso 1.00
+H19 H 0.65640 0.53660 0.42240 0.01267 Uiso 1.00
+H20 H 0.79410 0.24430 0.31040 0.01267 Uiso 1.00
+H21 H 0.11420 0.23400 0.57760 0.01267 Uiso 1.00
+H22 H 0.93390 0.48370 0.68960 0.01267 Uiso 1.00
+H23 H 0.34360 0.76600 0.88020 0.01267 Uiso 1.00
+H24 H 0.20590 0.51630 0.45020 0.01267 Uiso 1.00
+H25 H 0.88580 0.11980 0.46340 0.01267 Uiso 1.00
+H26 H 0.06610 0.54980 0.75570 0.01267 Uiso 1.00
+H27 H 0.65640 0.42240 0.53660 0.01267 Uiso 1.00
+H28 H 0.79410 0.31040 0.24430 0.01267 Uiso 1.00
+H29 H 0.11420 0.57760 0.23400 0.01267 Uiso 1.00
+H30 H 0.93390 0.68960 0.48370 0.01267 Uiso 1.00
+H31 H 0.34360 0.88020 0.76600 0.01267 Uiso 1.00
+H32 H 0.20590 0.45020 0.51630 0.01267 Uiso 1.00
+H33 H 0.42240 0.53660 0.65640 0.01267 Uiso 1.00
+H34 H 0.31040 0.24430 0.79410 0.01267 Uiso 1.00
+H35 H 0.11980 0.46340 0.88580 0.01267 Uiso 1.00
+H36 H 0.54980 0.75570 0.06610 0.01267 Uiso 1.00
+H37 H 0.88020 0.76600 0.34360 0.01267 Uiso 1.00
+H38 H 0.45020 0.51630 0.20590 0.01267 Uiso 1.00
+H39 H 0.57760 0.23400 0.11420 0.01267 Uiso 1.00
+H40 H 0.68960 0.48370 0.93390 0.01267 Uiso 1.00
+H41 H 0.76600 0.34360 0.88020 0.01267 Uiso 1.00
+H42 H 0.51630 0.20590 0.45020 0.01267 Uiso 1.00
+H43 H 0.23400 0.11420 0.57760 0.01267 Uiso 1.00
+H44 H 0.48370 0.93390 0.68960 0.01267 Uiso 1.00
+H45 H 0.53660 0.65640 0.42240 0.01267 Uiso 1.00
+H46 H 0.24430 0.79410 0.31040 0.01267 Uiso 1.00
+H47 H 0.46340 0.88580 0.11980 0.01267 Uiso 1.00
+H48 H 0.75570 0.06610 0.54980 0.01267 Uiso 1.00
+H49 H 0.37010 0.88180 0.37010 0.01267 Uiso 1.00
+H50 H 0.51170 0.00000 0.62990 0.01267 Uiso 1.00
+H51 H 0.48830 0.11820 0.48830 0.01267 Uiso 1.00
+H52 H 0.62990 0.00000 0.51170 0.01267 Uiso 1.00
+H53 H 0.48830 0.48830 0.11820 0.01267 Uiso 1.00
+H54 H 0.51170 0.62990 -0.00000 0.01267 Uiso 1.00
+H55 H 0.37010 0.37010 0.88180 0.01267 Uiso 1.00
+H56 H 0.62990 0.51170 -0.00000 0.01267 Uiso 1.00
+H57 H 0.00000 0.62990 0.51170 0.01267 Uiso 1.00
+H58 H 0.88180 0.37010 0.37010 0.01267 Uiso 1.00
+H59 H 0.00000 0.51170 0.62990 0.01267 Uiso 1.00
+H60 H 0.11820 0.48830 0.48830 0.01267 Uiso 1.00
+C1 C 0.41456 0.68356 0.47280 0.01267 Uiso 1.00
+C2 C 0.21076 0.94176 0.52720 0.01267 Uiso 1.00
+C3 C 0.78924 0.31644 0.73100 0.01267 Uiso 1.00
+C4 C 0.58544 0.05824 0.26900 0.01267 Uiso 1.00
+C5 C 0.73100 0.78924 0.31644 0.01267 Uiso 1.00
+C6 C 0.26900 0.58544 0.05824 0.01267 Uiso 1.00
+C7 C 0.47280 0.41456 0.68356 0.01267 Uiso 1.00
+C8 C 0.52720 0.21076 0.94176 0.01267 Uiso 1.00
+C9 C 0.94176 0.52720 0.21076 0.01267 Uiso 1.00
+C10 C 0.68356 0.47280 0.41456 0.01267 Uiso 1.00
+C11 C 0.05824 0.26900 0.58544 0.01267 Uiso 1.00
+C12 C 0.31644 0.73100 0.78924 0.01267 Uiso 1.00
+C13 C 0.94176 0.21076 0.52720 0.01267 Uiso 1.00
+C14 C 0.68356 0.41456 0.47280 0.01267 Uiso 1.00
+C15 C 0.05824 0.58544 0.26900 0.01267 Uiso 1.00
+C16 C 0.31644 0.78924 0.73100 0.01267 Uiso 1.00
+C17 C 0.41456 0.47280 0.68356 0.01267 Uiso 1.00
+C18 C 0.21076 0.52720 0.94176 0.01267 Uiso 1.00
+C19 C 0.78924 0.73100 0.31644 0.01267 Uiso 1.00
+C20 C 0.58544 0.26900 0.05824 0.01267 Uiso 1.00
+C21 C 0.73100 0.31644 0.78924 0.01267 Uiso 1.00
+C22 C 0.26900 0.05824 0.58544 0.01267 Uiso 1.00
+C23 C 0.47280 0.68356 0.41456 0.01267 Uiso 1.00
+C24 C 0.52720 0.94176 0.21076 0.01267 Uiso 1.00
+C25 C 0.37300 0.75680 0.37300 0.01267 Uiso 1.00
+C26 C 0.31950 0.80580 0.31950 0.01267 Uiso 1.00
+C27 C 0.38380 1.00000 0.62700 0.01267 Uiso 1.00
+C28 C 0.48630 1.00000 0.68050 0.01267 Uiso 1.00
+C29 C 0.61620 0.24320 0.61620 0.01267 Uiso 1.00
+C30 C 0.51370 0.19420 0.51370 0.01267 Uiso 1.00
+C31 C 0.62700 -0.00000 0.38380 0.01267 Uiso 1.00
+C32 C 0.68050 0.00000 0.48630 0.01267 Uiso 1.00
+C33 C 0.61620 0.61620 0.24320 0.01267 Uiso 1.00
+C34 C 0.51370 0.51370 0.19420 0.01267 Uiso 1.00
+C35 C 0.38380 0.62700 -0.00000 0.01267 Uiso 1.00
+C36 C 0.48630 0.68050 -0.00000 0.01267 Uiso 1.00
+C37 C 0.37300 0.37300 0.75680 0.01267 Uiso 1.00
+C38 C 0.31950 0.31950 0.80580 0.01267 Uiso 1.00
+C39 C 0.62700 0.38380 -0.00000 0.01267 Uiso 1.00
+C40 C 0.68050 0.48630 -0.00000 0.01267 Uiso 1.00
+C41 C 0.00000 0.62700 0.38380 0.01267 Uiso 1.00
+C42 C 0.00000 0.68050 0.48630 0.01267 Uiso 1.00
+C43 C 0.75680 0.37300 0.37300 0.01267 Uiso 1.00
+C44 C 0.80580 0.31950 0.31950 0.01267 Uiso 1.00
+C45 C 0.00000 0.38380 0.62700 0.01267 Uiso 1.00
+C46 C 0.00000 0.48630 0.68050 0.01267 Uiso 1.00
+C47 C 0.24320 0.61620 0.61620 0.01267 Uiso 1.00
+C48 C 0.19420 0.51370 0.51370 0.01267 Uiso 1.00
+N1 N 0.35145 0.72798 0.44533 0.01267 Uiso 1.00
+N2 N 0.28265 0.90612 0.55467 0.01267 Uiso 1.00
+N3 N 0.71735 0.27202 0.62347 0.01267 Uiso 1.00
+N4 N 0.64855 0.09388 0.37653 0.01267 Uiso 1.00
+N5 N 0.62347 0.71735 0.27202 0.01267 Uiso 1.00
+N6 N 0.37653 0.64855 0.09388 0.01267 Uiso 1.00
+N7 N 0.44533 0.35145 0.72798 0.01267 Uiso 1.00
+N8 N 0.55467 0.28265 0.90612 0.01267 Uiso 1.00
+N9 N 0.90612 0.55467 0.28265 0.01267 Uiso 1.00
+N10 N 0.72798 0.44533 0.35145 0.01267 Uiso 1.00
+N11 N 0.09388 0.37653 0.64855 0.01267 Uiso 1.00
+N12 N 0.27202 0.62347 0.71735 0.01267 Uiso 1.00
+N13 N 0.90612 0.28265 0.55467 0.01267 Uiso 1.00
+N14 N 0.72798 0.35145 0.44533 0.01267 Uiso 1.00
+N15 N 0.09388 0.64855 0.37653 0.01267 Uiso 1.00
+N16 N 0.27202 0.71735 0.62347 0.01267 Uiso 1.00
+N17 N 0.35145 0.44533 0.72798 0.01267 Uiso 1.00
+N18 N 0.28265 0.55467 0.90612 0.01267 Uiso 1.00
+N19 N 0.71735 0.62347 0.27202 0.01267 Uiso 1.00
+N20 N 0.64855 0.37653 0.09388 0.01267 Uiso 1.00
+N21 N 0.62347 0.27202 0.71735 0.01267 Uiso 1.00
+N22 N 0.37653 0.09388 0.64855 0.01267 Uiso 1.00
+N23 N 0.44533 0.72798 0.35145 0.01267 Uiso 1.00
+N24 N 0.55467 0.90612 0.28265 0.01267 Uiso 1.00
diff --git a/benchmarks/mof/structures/Zn-MOF-74.cif b/benchmarks/mof/structures/Zn-MOF-74.cif
new file mode 100644
index 0000000000000000000000000000000000000000..953e34ed02614ec1700b98900c6193935f42c29d
--- /dev/null
+++ b/benchmarks/mof/structures/Zn-MOF-74.cif
@@ -0,0 +1,186 @@
+data_image0
+_cell_length_a 26.179
+_cell_length_b 26.179
+_cell_length_c 6.652
+_cell_angle_alpha 90
+_cell_angle_beta 90
+_cell_angle_gamma 120
+
+_symmetry_space_group_name_H-M "P 1"
+_symmetry_int_tables_number 1
+
+loop_
+ _symmetry_equiv_pos_as_xyz
+ 'x, y, z'
+
+loop_
+ _atom_site_label
+ _atom_site_occupancy
+ _atom_site_fract_x
+ _atom_site_fract_y
+ _atom_site_fract_z
+ _atom_site_thermal_displace_type
+ _atom_site_B_iso_or_equiv
+ _atom_site_type_symbol
+ Zn1 1.0000 0.38790 0.35068 0.15211 Biso 1.000 Zn
+ C1 1.0000 0.32290 0.20386 0.28779 Biso 1.000 C
+ C2 1.0000 0.34291 0.22040 0.09164 Biso 1.000 C
+ C3 1.0000 0.35335 0.18320 0.97052 Biso 1.000 C
+ H1 1.0000 0.36889 0.19605 0.81817 Biso 1.000 H
+ C4 1.0000 0.31168 0.24386 0.41805 Biso 1.000 C
+ O1 1.0000 0.32032 0.29230 0.35040 Biso 1.000 O
+ O2 1.0000 0.29364 0.22895 0.59490 Biso 1.000 O
+ O3 1.0000 0.35220 0.27243 0.01900 Biso 1.000 O
+ Zn2 1.0000 0.05457 0.68401 0.48544 Biso 1.000 Zn
+ C5 1.0000 0.98957 0.53719 0.62112 Biso 1.000 C
+ C6 1.0000 0.00958 0.55373 0.42497 Biso 1.000 C
+ C7 1.0000 0.02002 0.51653 0.30385 Biso 1.000 C
+ H2 1.0000 0.03556 0.52938 0.15150 Biso 1.000 H
+ C8 1.0000 0.97835 0.57719 0.75138 Biso 1.000 C
+ O4 1.0000 0.98699 0.62563 0.68373 Biso 1.000 O
+ O5 1.0000 0.96031 0.56228 0.92823 Biso 1.000 O
+ O6 1.0000 0.01887 0.60576 0.35233 Biso 1.000 O
+ Zn3 1.0000 0.72123 0.01735 0.81878 Biso 1.000 Zn
+ C9 1.0000 0.65623 0.87053 0.95446 Biso 1.000 C
+ C10 1.0000 0.67624 0.88707 0.75831 Biso 1.000 C
+ C11 1.0000 0.68668 0.84987 0.63719 Biso 1.000 C
+ H3 1.0000 0.70222 0.86272 0.48484 Biso 1.000 H
+ C12 1.0000 0.64501 0.91053 0.08472 Biso 1.000 C
+ O7 1.0000 0.65365 0.95897 0.01707 Biso 1.000 O
+ O8 1.0000 0.62697 0.89562 0.26157 Biso 1.000 O
+ O9 1.0000 0.68553 0.93910 0.68567 Biso 1.000 O
+ Zn4 1.0000 0.64932 0.03722 0.15211 Biso 1.000 Zn
+ C13 1.0000 0.79614 0.11904 0.28779 Biso 1.000 C
+ C14 1.0000 0.77960 0.12251 0.09164 Biso 1.000 C
+ C15 1.0000 0.81680 0.17015 0.97052 Biso 1.000 C
+ H4 1.0000 0.80395 0.17284 0.81817 Biso 1.000 H
+ C16 1.0000 0.75614 0.06782 0.41805 Biso 1.000 C
+ O10 1.0000 0.70770 0.02802 0.35040 Biso 1.000 O
+ O11 1.0000 0.77105 0.06469 0.59490 Biso 1.000 O
+ O12 1.0000 0.72757 0.07977 0.01900 Biso 1.000 O
+ Zn5 1.0000 0.31599 0.37055 0.48544 Biso 1.000 Zn
+ C17 1.0000 0.46281 0.45237 0.62112 Biso 1.000 C
+ C18 1.0000 0.44627 0.45584 0.42497 Biso 1.000 C
+ C19 1.0000 0.48347 0.50348 0.30385 Biso 1.000 C
+ H5 1.0000 0.47062 0.50617 0.15150 Biso 1.000 H
+ C20 1.0000 0.42281 0.40115 0.75138 Biso 1.000 C
+ O13 1.0000 0.37437 0.36135 0.68373 Biso 1.000 O
+ O14 1.0000 0.43772 0.39802 0.92823 Biso 1.000 O
+ O15 1.0000 0.39424 0.41310 0.35233 Biso 1.000 O
+ Zn6 1.0000 0.98265 0.70389 0.81878 Biso 1.000 Zn
+ C21 1.0000 0.12947 0.78571 0.95446 Biso 1.000 C
+ C22 1.0000 0.11293 0.78918 0.75831 Biso 1.000 C
+ C23 1.0000 0.15013 0.83682 0.63719 Biso 1.000 C
+ H6 1.0000 0.13728 0.83951 0.48484 Biso 1.000 H
+ C24 1.0000 0.08947 0.73449 0.08472 Biso 1.000 C
+ O16 1.0000 0.04103 0.69469 0.01707 Biso 1.000 O
+ O17 1.0000 0.10438 0.73136 0.26157 Biso 1.000 O
+ O18 1.0000 0.06090 0.74644 0.68567 Biso 1.000 O
+ Zn7 1.0000 0.96278 0.61210 0.15211 Biso 1.000 Zn
+ C25 1.0000 0.88096 0.67710 0.28779 Biso 1.000 C
+ C26 1.0000 0.87749 0.65709 0.09164 Biso 1.000 C
+ C27 1.0000 0.82985 0.64665 0.97052 Biso 1.000 C
+ H7 1.0000 0.82716 0.63111 0.81817 Biso 1.000 H
+ C28 1.0000 0.93218 0.68832 0.41805 Biso 1.000 C
+ O19 1.0000 0.97198 0.67968 0.35040 Biso 1.000 O
+ O20 1.0000 0.93531 0.70636 0.59490 Biso 1.000 O
+ O21 1.0000 0.92023 0.64780 0.01900 Biso 1.000 O
+ Zn8 1.0000 0.62945 0.94543 0.48544 Biso 1.000 Zn
+ C29 1.0000 0.54763 0.01043 0.62112 Biso 1.000 C
+ C30 1.0000 0.54416 0.99042 0.42497 Biso 1.000 C
+ C31 1.0000 0.49652 0.97998 0.30385 Biso 1.000 C
+ H8 1.0000 0.49383 0.96444 0.15150 Biso 1.000 H
+ C32 1.0000 0.59885 0.02165 0.75138 Biso 1.000 C
+ O22 1.0000 0.63865 0.01301 0.68373 Biso 1.000 O
+ O23 1.0000 0.60198 0.03969 0.92823 Biso 1.000 O
+ O24 1.0000 0.58690 0.98113 0.35233 Biso 1.000 O
+ Zn9 1.0000 0.29611 0.27877 0.81878 Biso 1.000 Zn
+ C33 1.0000 0.21429 0.34377 0.95446 Biso 1.000 C
+ C34 1.0000 0.21082 0.32376 0.75831 Biso 1.000 C
+ C35 1.0000 0.16318 0.31332 0.63719 Biso 1.000 C
+ H9 1.0000 0.16049 0.29778 0.48484 Biso 1.000 H
+ C36 1.0000 0.26551 0.35499 0.08472 Biso 1.000 C
+ O25 1.0000 0.30531 0.34635 0.01707 Biso 1.000 O
+ O26 1.0000 0.26864 0.37303 0.26157 Biso 1.000 O
+ O27 1.0000 0.25356 0.31447 0.68567 Biso 1.000 O
+ Zn10 1.0000 0.61210 0.64932 0.84789 Biso 1.000 Zn
+ C37 1.0000 0.67710 0.79614 0.71221 Biso 1.000 C
+ C38 1.0000 0.65709 0.77960 0.90836 Biso 1.000 C
+ C39 1.0000 0.64665 0.81680 0.02948 Biso 1.000 C
+ H10 1.0000 0.63111 0.80395 0.18183 Biso 1.000 H
+ C40 1.0000 0.68832 0.75614 0.58195 Biso 1.000 C
+ O28 1.0000 0.67968 0.70770 0.64960 Biso 1.000 O
+ O29 1.0000 0.70636 0.77105 0.40510 Biso 1.000 O
+ O30 1.0000 0.64780 0.72757 0.98100 Biso 1.000 O
+ Zn11 1.0000 0.27877 0.98265 0.18122 Biso 1.000 Zn
+ C41 1.0000 0.34377 0.12947 0.04554 Biso 1.000 C
+ C42 1.0000 0.32376 0.11293 0.24169 Biso 1.000 C
+ C43 1.0000 0.31332 0.15013 0.36281 Biso 1.000 C
+ H11 1.0000 0.29778 0.13728 0.51516 Biso 1.000 H
+ C44 1.0000 0.35499 0.08947 0.91528 Biso 1.000 C
+ O31 1.0000 0.34635 0.04103 0.98293 Biso 1.000 O
+ O32 1.0000 0.37303 0.10438 0.73843 Biso 1.000 O
+ O33 1.0000 0.31447 0.06090 0.31433 Biso 1.000 O
+ Zn12 1.0000 0.94543 0.31599 0.51456 Biso 1.000 Zn
+ C45 1.0000 0.01043 0.46281 0.37888 Biso 1.000 C
+ C46 1.0000 0.99042 0.44627 0.57503 Biso 1.000 C
+ C47 1.0000 0.97998 0.48347 0.69615 Biso 1.000 C
+ H12 1.0000 0.96444 0.47062 0.84850 Biso 1.000 H
+ C48 1.0000 0.02165 0.42281 0.24862 Biso 1.000 C
+ O34 1.0000 0.01301 0.37437 0.31627 Biso 1.000 O
+ O35 1.0000 0.03969 0.43772 0.07177 Biso 1.000 O
+ O36 1.0000 0.98113 0.39424 0.64767 Biso 1.000 O
+ Zn13 1.0000 0.35068 0.96278 0.84789 Biso 1.000 Zn
+ C49 1.0000 0.20386 0.88096 0.71221 Biso 1.000 C
+ C50 1.0000 0.22040 0.87749 0.90836 Biso 1.000 C
+ C51 1.0000 0.18320 0.82985 0.02948 Biso 1.000 C
+ H13 1.0000 0.19605 0.82716 0.18183 Biso 1.000 H
+ C52 1.0000 0.24386 0.93218 0.58195 Biso 1.000 C
+ O37 1.0000 0.29230 0.97198 0.64960 Biso 1.000 O
+ O38 1.0000 0.22895 0.93531 0.40510 Biso 1.000 O
+ O39 1.0000 0.27243 0.92023 0.98100 Biso 1.000 O
+ Zn14 1.0000 0.01735 0.29611 0.18122 Biso 1.000 Zn
+ C53 1.0000 0.87053 0.21429 0.04554 Biso 1.000 C
+ C54 1.0000 0.88707 0.21082 0.24169 Biso 1.000 C
+ C55 1.0000 0.84987 0.16318 0.36281 Biso 1.000 C
+ H14 1.0000 0.86272 0.16049 0.51516 Biso 1.000 H
+ C56 1.0000 0.91053 0.26551 0.91528 Biso 1.000 C
+ O40 1.0000 0.95897 0.30531 0.98293 Biso 1.000 O
+ O41 1.0000 0.89562 0.26864 0.73843 Biso 1.000 O
+ O42 1.0000 0.93910 0.25356 0.31433 Biso 1.000 O
+ Zn15 1.0000 0.68401 0.62945 0.51456 Biso 1.000 Zn
+ C57 1.0000 0.53719 0.54763 0.37888 Biso 1.000 C
+ C58 1.0000 0.55373 0.54416 0.57503 Biso 1.000 C
+ C59 1.0000 0.51653 0.49652 0.69615 Biso 1.000 C
+ H15 1.0000 0.52938 0.49383 0.84850 Biso 1.000 H
+ C60 1.0000 0.57719 0.59885 0.24862 Biso 1.000 C
+ O43 1.0000 0.62563 0.63865 0.31627 Biso 1.000 O
+ O44 1.0000 0.56228 0.60198 0.07177 Biso 1.000 O
+ O45 1.0000 0.60576 0.58690 0.64767 Biso 1.000 O
+ Zn16 1.0000 0.03722 0.38790 0.84789 Biso 1.000 Zn
+ C61 1.0000 0.11904 0.32290 0.71221 Biso 1.000 C
+ C62 1.0000 0.12251 0.34291 0.90836 Biso 1.000 C
+ C63 1.0000 0.17015 0.35335 0.02948 Biso 1.000 C
+ H16 1.0000 0.17284 0.36889 0.18183 Biso 1.000 H
+ C64 1.0000 0.06782 0.31168 0.58195 Biso 1.000 C
+ O46 1.0000 0.02802 0.32032 0.64960 Biso 1.000 O
+ O47 1.0000 0.06469 0.29364 0.40510 Biso 1.000 O
+ O48 1.0000 0.07977 0.35220 0.98100 Biso 1.000 O
+ Zn17 1.0000 0.70389 0.72123 0.18122 Biso 1.000 Zn
+ C65 1.0000 0.78571 0.65623 0.04554 Biso 1.000 C
+ C66 1.0000 0.78918 0.67624 0.24169 Biso 1.000 C
+ C67 1.0000 0.83682 0.68668 0.36281 Biso 1.000 C
+ H17 1.0000 0.83951 0.70222 0.51516 Biso 1.000 H
+ C68 1.0000 0.73449 0.64501 0.91528 Biso 1.000 C
+ O49 1.0000 0.69469 0.65365 0.98293 Biso 1.000 O
+ O50 1.0000 0.73136 0.62697 0.73843 Biso 1.000 O
+ O51 1.0000 0.74644 0.68553 0.31433 Biso 1.000 O
+ Zn18 1.0000 0.37055 0.05457 0.51456 Biso 1.000 Zn
+ C69 1.0000 0.45237 0.98957 0.37888 Biso 1.000 C
+ C70 1.0000 0.45584 0.00958 0.57503 Biso 1.000 C
+ C71 1.0000 0.50348 0.02002 0.69615 Biso 1.000 C
+ H18 1.0000 0.50617 0.03556 0.84850 Biso 1.000 H
+ C72 1.0000 0.40115 0.97835 0.24862 Biso 1.000 C
+ O52 1.0000 0.36135 0.98699 0.31627 Biso 1.000 O
+ O53 1.0000 0.39802 0.96031 0.07177 Biso 1.000 O
+ O54 1.0000 0.41310 0.01887 0.64767 Biso 1.000 O
diff --git a/benchmarks/mof/structures/dac/CFA-1-OH(Zn).cif b/benchmarks/mof/structures/dac/CFA-1-OH(Zn).cif
new file mode 100644
index 0000000000000000000000000000000000000000..a3ff22601cb7723f498e7708d58de1a53faebc47
--- /dev/null
+++ b/benchmarks/mof/structures/dac/CFA-1-OH(Zn).cif
@@ -0,0 +1,194 @@
+data_Zn-CFA-1-OH
+_audit_creation_date 2024-02-27
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 17.7500
+_cell_length_b 17.7500
+_cell_length_c 19.1920
+_cell_angle_alpha 90.0000
+_cell_angle_beta 90.0000
+_cell_angle_gamma 120.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+C1 C 0.13251 0.47278 0.60099 0.01267 Uiso 1.00
+C2 C 0.52717 0.65924 0.60145 0.01267 Uiso 1.00
+C3 C 0.34054 0.86768 0.60110 0.01267 Uiso 1.00
+C4 C 0.47278 0.13251 0.39901 0.01267 Uiso 1.00
+C5 C 0.65924 0.52717 0.39855 0.01267 Uiso 1.00
+C6 C 0.86768 0.34054 0.39890 0.01267 Uiso 1.00
+C7 C 0.18881 0.43840 0.60309 0.01267 Uiso 1.00
+C8 C 0.56134 0.74988 0.60338 0.01267 Uiso 1.00
+C9 C 0.24988 0.81128 0.60314 0.01267 Uiso 1.00
+C10 C 0.43840 0.18881 0.39691 0.01267 Uiso 1.00
+C11 C 0.74988 0.56134 0.39662 0.01267 Uiso 1.00
+C12 C 0.81128 0.24988 0.39686 0.01267 Uiso 1.00
+C13 C 0.16934 0.36248 0.56642 0.01267 Uiso 1.00
+C14 C 0.63720 0.80647 0.56673 0.01267 Uiso 1.00
+C15 C 0.19332 0.83058 0.56649 0.01267 Uiso 1.00
+C16 C 0.36248 0.16934 0.43358 0.01267 Uiso 1.00
+C17 C 0.80647 0.63720 0.43327 0.01267 Uiso 1.00
+C18 C 0.83058 0.19332 0.43351 0.01267 Uiso 1.00
+C19 C 0.09438 0.32519 0.52709 0.01267 Uiso 1.00
+C20 C 0.67462 0.76899 0.52736 0.01267 Uiso 1.00
+C21 C 0.23088 0.90552 0.52715 0.01267 Uiso 1.00
+C22 C 0.32519 0.09438 0.47291 0.01267 Uiso 1.00
+C23 C 0.76899 0.67462 0.47264 0.01267 Uiso 1.00
+C24 C 0.90551 0.23088 0.47286 0.01267 Uiso 1.00
+C25 C 0.03841 0.36098 0.52299 0.01267 Uiso 1.00
+C26 C 0.63892 0.67723 0.52316 0.01267 Uiso 1.00
+C27 C 0.32263 0.96162 0.52307 0.01267 Uiso 1.00
+C28 C 0.36098 0.03841 0.47701 0.01267 Uiso 1.00
+C29 C 0.67723 0.63892 0.47685 0.01267 Uiso 1.00
+C30 C 0.96162 0.32263 0.47693 0.01267 Uiso 1.00
+C31 C 0.05648 0.43436 0.56136 0.01267 Uiso 1.00
+C32 C 0.56569 0.62180 0.56167 0.01267 Uiso 1.00
+C33 C 0.37804 0.94371 0.56146 0.01267 Uiso 1.00
+C34 C 0.43436 0.05648 0.43864 0.01267 Uiso 1.00
+C35 C 0.62180 0.56569 0.43833 0.01267 Uiso 1.00
+C36 C 0.94371 0.37804 0.43854 0.01267 Uiso 1.00
+C37 C 0.09165 0.61513 0.83198 0.01267 Uiso 1.00
+C38 C 0.38223 0.47632 0.83322 0.01267 Uiso 1.00
+C39 C 0.52377 0.90807 0.83233 0.01267 Uiso 1.00
+C40 C 0.61513 0.09165 0.16802 0.01267 Uiso 1.00
+C41 C 0.47632 0.38224 0.16679 0.01267 Uiso 1.00
+C42 C 0.90807 0.52377 0.16767 0.01267 Uiso 1.00
+C43 C 0.14157 0.62308 0.89145 0.01267 Uiso 1.00
+C44 C 0.37545 0.51853 0.89300 0.01267 Uiso 1.00
+C45 C 0.48126 0.85826 0.89172 0.01267 Uiso 1.00
+C46 C 0.62308 0.14157 0.10855 0.01267 Uiso 1.00
+C47 C 0.51853 0.37545 0.10700 0.01267 Uiso 1.00
+C48 C 0.85826 0.48126 0.10828 0.01267 Uiso 1.00
+C49 C 0.10720 0.60993 0.95871 0.01267 Uiso 1.00
+C50 C 0.38919 0.49669 0.95983 0.01267 Uiso 1.00
+C51 C 0.50192 0.89238 0.95910 0.01267 Uiso 1.00
+C52 C 0.60993 0.10720 0.04130 0.01267 Uiso 1.00
+C53 C 0.49669 0.38919 0.04017 0.01267 Uiso 1.00
+C54 C 0.89238 0.50192 0.04090 0.01267 Uiso 1.00
+C55 C 0.02065 0.58763 0.96512 0.01267 Uiso 1.00
+C56 C 0.40977 0.43115 0.96556 0.01267 Uiso 1.00
+C57 C 0.56671 0.97874 0.96561 0.01267 Uiso 1.00
+C58 C 0.58763 0.02065 0.03488 0.01267 Uiso 1.00
+C59 C 0.43115 0.40977 0.03444 0.01267 Uiso 1.00
+C60 C 0.97874 0.56671 0.03439 0.01267 Uiso 1.00
+C61 C 0.97123 0.58133 0.90446 0.01267 Uiso 1.00
+C62 C 0.41439 0.38765 0.90455 0.01267 Uiso 1.00
+C63 C 0.61033 0.02818 0.90506 0.01267 Uiso 1.00
+C64 C 0.58132 0.97123 0.09554 0.01267 Uiso 1.00
+C65 C 0.38765 0.41439 0.09545 0.01267 Uiso 1.00
+C66 C 0.02818 0.61033 0.09494 0.01267 Uiso 1.00
+C67 C 0.00475 0.59477 0.83802 0.01267 Uiso 1.00
+C68 C 0.40121 0.40880 0.83841 0.01267 Uiso 1.00
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+C70 C 0.59477 0.00475 0.16198 0.01267 Uiso 1.00
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+N149 N 0.41996 0.77840 0.86857 0.01267 Uiso 1.00
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+N151 N 0.58043 0.35736 0.12947 0.01267 Uiso 1.00
+N152 N 0.77840 0.41996 0.13143 0.01267 Uiso 1.00
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+O157 O 0.40473 0.39279 0.35148 0.01267 Uiso 1.00
+O158 O 0.01200 0.60710 0.64498 0.01267 Uiso 1.00
+O159 O 0.59581 0.98814 0.64599 0.01267 Uiso 1.00
+O160 O 0.39279 0.40473 0.64852 0.01267 Uiso 1.00
+Zn161 Zn 0.33310 0.66750 0.73393 0.01267 Uiso 1.00
+Zn162 Zn 0.66750 0.33310 0.26607 0.01267 Uiso 1.00
+Zn163 Zn 0.33330 0.66822 0.92337 0.01267 Uiso 1.00
+Zn164 Zn 0.66822 0.33330 0.07663 0.01267 Uiso 1.00
+Zn165 Zn 0.12297 0.62905 0.67054 0.01267 Uiso 1.00
+Zn166 Zn 0.37052 0.49432 0.67149 0.01267 Uiso 1.00
+Zn167 Zn 0.50635 0.87704 0.67074 0.01267 Uiso 1.00
+Zn168 Zn 0.62905 0.12297 0.32946 0.01267 Uiso 1.00
+Zn169 Zn 0.49432 0.37052 0.32851 0.01267 Uiso 1.00
+Zn170 Zn 0.87704 0.50635 0.32926 0.01267 Uiso 1.00
diff --git a/benchmarks/mof/structures/dac/NbOFFIVE-1-Ni.cif b/benchmarks/mof/structures/dac/NbOFFIVE-1-Ni.cif
new file mode 100644
index 0000000000000000000000000000000000000000..785ba17d0ab68c7cfdeceacc15b93774c0832d42
--- /dev/null
+++ b/benchmarks/mof/structures/dac/NbOFFIVE-1-Ni.cif
@@ -0,0 +1,136 @@
+data_image0
+_cell_length_a 9.8884
+_cell_length_b 9.8884
+_cell_length_c 15.7829
+_cell_angle_alpha 90
+_cell_angle_beta 90
+_cell_angle_gamma 90
+
+_symmetry_space_group_name_H-M "P 1"
+_symmetry_int_tables_number 1
+
+loop_
+ _symmetry_equiv_pos_as_xyz
+ 'x, y, z'
+
+loop_
+ _atom_site_label
+ _atom_site_occupancy
+ _atom_site_fract_x
+ _atom_site_fract_y
+ _atom_site_fract_z
+ _atom_site_thermal_displace_type
+ _atom_site_B_iso_or_equiv
+ _atom_site_type_symbol
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+ H2 1.0000 0.74100 0.59370 0.64520 Biso 1.000 H
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+ H5 1.0000 0.90630 0.24100 0.14520 Biso 1.000 H
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+ H6 1.0000 0.40630 0.74100 0.64520 Biso 1.000 H
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+ H21 1.0000 0.09370 0.75900 0.85480 Biso 1.000 H
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+ H22 1.0000 0.59370 0.25900 0.35480 Biso 1.000 H
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+ H23 1.0000 0.90630 0.24100 0.85480 Biso 1.000 H
+ C24 1.0000 0.34290 0.74340 0.31130 Biso 1.000 C
+ H24 1.0000 0.40630 0.74100 0.35480 Biso 1.000 H
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+ Ni1 1.0000 0.00000 0.00000 0.25000 Biso 1.000 Ni
+ Ni2 1.0000 0.50000 0.50000 0.75000 Biso 1.000 Ni
+ Ni3 1.0000 0.00000 0.00000 0.75000 Biso 1.000 Ni
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+ N6 1.0000 0.34820 0.65180 0.75000 Biso 1.000 N
+ N7 1.0000 0.15180 0.84820 0.25000 Biso 1.000 N
+ N8 1.0000 0.65180 0.34820 0.75000 Biso 1.000 N
+ N9 1.0000 0.84820 0.84820 0.75000 Biso 1.000 N
+ N10 1.0000 0.34820 0.34820 0.25000 Biso 1.000 N
+ N11 1.0000 0.15180 0.15180 0.75000 Biso 1.000 N
+ N12 1.0000 0.65180 0.65180 0.25000 Biso 1.000 N
+ N13 1.0000 0.15180 0.84820 0.75000 Biso 1.000 N
+ N14 1.0000 0.65180 0.34820 0.25000 Biso 1.000 N
+ N15 1.0000 0.84820 0.15180 0.75000 Biso 1.000 N
+ N16 1.0000 0.34820 0.65180 0.25000 Biso 1.000 N
diff --git a/benchmarks/mof/structures/dac/SGU-29.cif b/benchmarks/mof/structures/dac/SGU-29.cif
new file mode 100644
index 0000000000000000000000000000000000000000..fb15f604b05ccdc345c547ca5cc415b848701fef
--- /dev/null
+++ b/benchmarks/mof/structures/dac/SGU-29.cif
@@ -0,0 +1,995 @@
+data_sgu-29b_1\(2)
+_audit_creation_date 2025-02-06
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 20.8200
+_cell_length_b 20.8190
+_cell_length_c 14.6970
+_cell_angle_alpha 90.0000
+_cell_angle_beta 110.7300
+_cell_angle_gamma 90.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
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+O2 O 0.37297 0.87246 0.00024 0.00000 Uiso 1.00
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+Si18 Si 0.43514 0.26839 0.39631 0.00000 Uiso 1.00
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+Si130 Si 0.31580 0.51989 0.10487 0.00000 Uiso 1.00
+Si131 Si 0.06486 0.76839 0.10369 0.00000 Uiso 1.00
+Si132 Si 0.99378 0.21051 0.39573 0.00000 Uiso 1.00
+Si133 Si 0.07889 0.91440 0.39580 0.00000 Uiso 1.00
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+Si166 Si 0.63794 0.14302 0.89614 0.00000 Uiso 1.00
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+Si168 Si 0.56486 0.73161 0.60369 0.00000 Uiso 1.00
+Si169 Si 0.49378 0.28949 0.89573 0.00000 Uiso 1.00
+Si170 Si 0.57889 0.58560 0.89580 0.00000 Uiso 1.00
+Si171 Si 0.88840 0.89500 0.89626 0.00000 Uiso 1.00
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+O177 O 0.80640 0.07990 0.89690 0.00000 Uiso 1.00
+O178 O 0.92490 0.96150 0.89610 0.00000 Uiso 1.00
+O179 O 0.86950 0.03960 0.63490 0.00000 Uiso 1.00
+O180 O 0.67910 0.20690 0.89560 0.00000 Uiso 1.00
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+Na186 Na 0.38750 0.56606 0.81347 0.00000 Uiso 1.00
+Na187 Na 0.35635 0.30876 0.68888 0.00000 Uiso 1.00
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+O189 O 0.12703 0.62754 0.99976 0.00000 Uiso 1.00
+O190 O 0.18918 0.39301 0.68477 0.00000 Uiso 1.00
+O191 O 0.06222 0.14160 0.81394 0.00000 Uiso 1.00
+O192 O 0.96951 0.73343 0.81351 0.00000 Uiso 1.00
+O193 O 0.06085 0.64320 0.81582 0.00000 Uiso 1.00
+O194 O 0.31191 0.39113 0.81438 0.00000 Uiso 1.00
+O195 O 0.28010 0.48388 0.68695 0.00000 Uiso 1.00
+O196 O 0.22229 0.48180 0.81669 0.00000 Uiso 1.00
+O197 O 0.02779 0.23194 0.68373 0.00000 Uiso 1.00
+O198 O 0.25338 0.50209 0.00114 0.00000 Uiso 1.00
+O199 O 0.00206 0.24711 0.49945 0.00000 Uiso 1.00
+O200 O 0.37799 0.37780 0.00081 0.00000 Uiso 1.00
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+Si202 Si 0.24238 0.53875 0.89661 0.00000 Uiso 1.00
+Si203 Si 0.13794 0.64302 0.89614 0.00000 Uiso 1.00
+Si204 Si 0.31580 0.48011 0.60487 0.00000 Uiso 1.00
+Si205 Si 0.06486 0.23161 0.60369 0.00000 Uiso 1.00
+Si206 Si 0.99378 0.78949 0.89573 0.00000 Uiso 1.00
+Si207 Si 0.07889 0.08560 0.89580 0.00000 Uiso 1.00
+Si208 Si 0.38840 0.39500 0.89626 0.00000 Uiso 1.00
+Si209 Si 0.16922 0.33597 0.60429 0.00000 Uiso 1.00
+O210 O 0.10610 0.16807 0.60380 0.00000 Uiso 1.00
+O211 O 0.17190 0.57970 0.86730 0.00000 Uiso 1.00
+O212 O 0.01220 0.04930 0.89550 0.00000 Uiso 1.00
+O213 O 0.11350 0.29460 0.63340 0.00000 Uiso 1.00
+O214 O 0.30640 0.57990 0.89690 0.00000 Uiso 1.00
+O215 O 0.42490 0.46150 0.89610 0.00000 Uiso 1.00
+O216 O 0.36950 0.53960 0.63490 0.00000 Uiso 1.00
+O217 O 0.17910 0.70690 0.89560 0.00000 Uiso 1.00
+O218 O 0.23330 0.29520 0.60460 0.00000 Uiso 1.00
+O219 O 0.35240 0.41346 0.60550 0.00000 Uiso 1.00
+O220 O 0.06000 0.82590 0.89490 0.00000 Uiso 1.00
+O221 O 0.92700 0.83610 0.86630 0.00000 Uiso 1.00
+Na222 Na 0.73579 0.68762 0.69330 0.00000 Uiso 1.00
+Na223 Na 0.88750 0.06606 0.81347 0.00000 Uiso 1.00
+Na224 Na 0.85635 0.80876 0.68888 0.00000 Uiso 1.00
+Na225 Na 0.01010 0.43710 0.79050 0.00000 Uiso 1.00
+Si226 Si 0.24909 0.93741 0.74949 0.00000 Uiso 1.00
+O227 O 0.37297 0.12754 0.50024 0.00000 Uiso 1.00
+O228 O 0.31082 0.89301 0.81523 0.00000 Uiso 1.00
+O229 O 0.43778 0.64160 0.68606 0.00000 Uiso 1.00
+O230 O 0.53049 0.23343 0.68649 0.00000 Uiso 1.00
+O231 O 0.43915 0.14320 0.68418 0.00000 Uiso 1.00
+O232 O 0.18809 0.89113 0.68562 0.00000 Uiso 1.00
+O233 O 0.21990 0.98388 0.81305 0.00000 Uiso 1.00
+O234 O 0.27771 0.98180 0.68331 0.00000 Uiso 1.00
+O235 O 0.47221 0.73194 0.81627 0.00000 Uiso 1.00
+O236 O 0.24662 0.00209 0.49886 0.00000 Uiso 1.00
+O237 O 0.49794 0.74711 0.00055 0.00000 Uiso 1.00
+O238 O 0.12201 0.87780 0.49919 0.00000 Uiso 1.00
+Cu239 Cu 0.37361 0.62672 0.00133 0.00000 Uiso 1.00
+Si240 Si 0.25762 0.03875 0.60339 0.00000 Uiso 1.00
+Si241 Si 0.36206 0.14302 0.60386 0.00000 Uiso 1.00
+Si242 Si 0.18420 0.98011 0.89513 0.00000 Uiso 1.00
+Si243 Si 0.43514 0.73161 0.89631 0.00000 Uiso 1.00
+Si244 Si 0.50622 0.28949 0.60427 0.00000 Uiso 1.00
+Si245 Si 0.42111 0.58560 0.60420 0.00000 Uiso 1.00
+Si246 Si 0.11160 0.89500 0.60374 0.00000 Uiso 1.00
+Si247 Si 0.33078 0.83597 0.89571 0.00000 Uiso 1.00
+O248 O 0.39390 0.66807 0.89620 0.00000 Uiso 1.00
+O249 O 0.32810 0.07970 0.63270 0.00000 Uiso 1.00
+O250 O 0.48780 0.54930 0.60450 0.00000 Uiso 1.00
+O251 O 0.38650 0.79460 0.86660 0.00000 Uiso 1.00
+O252 O 0.19360 0.07990 0.60310 0.00000 Uiso 1.00
+O253 O 0.07510 0.96150 0.60390 0.00000 Uiso 1.00
+O254 O 0.13050 0.03960 0.86510 0.00000 Uiso 1.00
+O255 O 0.32090 0.20690 0.60440 0.00000 Uiso 1.00
+O256 O 0.26670 0.79520 0.89540 0.00000 Uiso 1.00
+O257 O 0.14760 0.91346 0.89450 0.00000 Uiso 1.00
+O258 O 0.44000 0.32590 0.60510 0.00000 Uiso 1.00
+O259 O 0.57300 0.33610 0.63370 0.00000 Uiso 1.00
+Na260 Na 0.76421 0.18762 0.80670 0.00000 Uiso 1.00
+Na261 Na 0.61250 0.56606 0.68653 0.00000 Uiso 1.00
+Na262 Na 0.64365 0.30876 0.81112 0.00000 Uiso 1.00
+Na263 Na 0.48990 0.93710 0.70950 0.00000 Uiso 1.00
+Si264 Si 0.74909 0.43741 0.74949 0.00000 Uiso 1.00
+O265 O 0.87297 0.62754 0.50024 0.00000 Uiso 1.00
+O266 O 0.81082 0.39301 0.81523 0.00000 Uiso 1.00
+O267 O 0.93778 0.14160 0.68606 0.00000 Uiso 1.00
+O268 O 0.03049 0.73343 0.68649 0.00000 Uiso 1.00
+O269 O 0.93915 0.64320 0.68418 0.00000 Uiso 1.00
+O270 O 0.68809 0.39113 0.68562 0.00000 Uiso 1.00
+O271 O 0.71990 0.48388 0.81305 0.00000 Uiso 1.00
+O272 O 0.77771 0.48180 0.68331 0.00000 Uiso 1.00
+O273 O 0.97221 0.23194 0.81627 0.00000 Uiso 1.00
+O274 O 0.74662 0.50209 0.49886 0.00000 Uiso 1.00
+O275 O 0.99794 0.24711 0.00055 0.00000 Uiso 1.00
+O276 O 0.62201 0.37780 0.49919 0.00000 Uiso 1.00
+Cu277 Cu 0.87361 0.12672 0.00133 0.00000 Uiso 1.00
+Si278 Si 0.75762 0.53875 0.60339 0.00000 Uiso 1.00
+Si279 Si 0.86206 0.64302 0.60386 0.00000 Uiso 1.00
+Si280 Si 0.68420 0.48011 0.89513 0.00000 Uiso 1.00
+Si281 Si 0.93514 0.23161 0.89631 0.00000 Uiso 1.00
+Si282 Si 0.00622 0.78949 0.60427 0.00000 Uiso 1.00
+Si283 Si 0.92111 0.08560 0.60420 0.00000 Uiso 1.00
+Si284 Si 0.61160 0.39500 0.60374 0.00000 Uiso 1.00
+Si285 Si 0.83078 0.33597 0.89571 0.00000 Uiso 1.00
+O286 O 0.89390 0.16807 0.89620 0.00000 Uiso 1.00
+O287 O 0.82810 0.57970 0.63270 0.00000 Uiso 1.00
+O288 O 0.98780 0.04930 0.60450 0.00000 Uiso 1.00
+O289 O 0.88650 0.29460 0.86660 0.00000 Uiso 1.00
+O290 O 0.69360 0.57990 0.60310 0.00000 Uiso 1.00
+O291 O 0.57510 0.46150 0.60390 0.00000 Uiso 1.00
+O292 O 0.63050 0.53960 0.86510 0.00000 Uiso 1.00
+O293 O 0.82090 0.70690 0.60440 0.00000 Uiso 1.00
+O294 O 0.76670 0.29520 0.89540 0.00000 Uiso 1.00
+O295 O 0.64760 0.41346 0.89450 0.00000 Uiso 1.00
+O296 O 0.94000 0.82590 0.60510 0.00000 Uiso 1.00
+O297 O 0.07300 0.83610 0.63370 0.00000 Uiso 1.00
+Na298 Na 0.26421 0.68762 0.80670 0.00000 Uiso 1.00
+Na299 Na 0.11250 0.06606 0.68653 0.00000 Uiso 1.00
+Na300 Na 0.14365 0.80876 0.81112 0.00000 Uiso 1.00
+Si301 Si 0.50000 0.31328 0.25000 0.00000 Uiso 1.00
+Si302 Si 0.50000 0.81163 0.25000 0.00000 Uiso 1.00
+Na303 Na 0.50000 0.56280 0.25000 0.00000 Uiso 1.00
+Si304 Si 0.00000 0.81328 0.25000 0.00000 Uiso 1.00
+Si305 Si 0.00000 0.31163 0.25000 0.00000 Uiso 1.00
+Na306 Na 0.00000 0.06280 0.25000 0.00000 Uiso 1.00
+Si307 Si 0.50000 0.68672 0.75000 0.00000 Uiso 1.00
+Si308 Si 0.50000 0.18837 0.75000 0.00000 Uiso 1.00
+Na309 Na 0.50000 0.43720 0.75000 0.00000 Uiso 1.00
+Si310 Si -0.00000 0.18672 0.75000 0.00000 Uiso 1.00
+Si311 Si -0.00000 0.68837 0.75000 0.00000 Uiso 1.00
+Na312 Na -0.00000 0.93720 0.75000 0.00000 Uiso 1.00
+Cu313 Cu 0.25000 0.75000 -0.00000 0.00000 Uiso 1.00
+Cu314 Cu 0.75000 0.75000 0.50000 0.00000 Uiso 1.00
+Cu315 Cu 0.25000 0.25000 0.50000 0.00000 Uiso 1.00
+Cu316 Cu 0.75000 0.25000 0.00000 0.00000 Uiso 1.00
+Cu317 Cu 0.50000 0.50000 -0.00000 0.00000 Uiso 1.00
+Cu318 Cu 0.00000 0.00000 0.00000 0.00000 Uiso 1.00
+Cu319 Cu 0.50000 0.50000 0.50000 0.00000 Uiso 1.00
+Cu320 Cu 0.00000 0.00000 0.50000 0.00000 Uiso 1.00
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_2
+_ccdc_geom_bond_type
+Si1 O3 1.600 . S
+Si1 O7 1.606 . S
+Si1 O8 1.606 . S
+Si1 O9 1.602 . S
+O2 Si16 1.649 . S
+O2 Si247 1.659 1_554 S
+O3 Si22 1.623 . S
+O4 Si20 1.622 . S
+O4 Si301 1.607 . S
+O5 Si19 1.626 . S
+O5 Si302 1.604 . S
+O6 Si16 1.621 . S
+O6 Si302 1.599 . S
+O7 Si21 1.623 . S
+O8 Si17 1.627 . S
+O9 Si15 1.616 1_545 S
+O10 Si18 1.619 . S
+O10 Si301 1.603 . S
+O11 Si15 1.657 1_556 S
+O11 Si242 1.658 . S
+O12 Si18 1.656 . S
+O12 Si244 1.658 . S
+O13 Si21 1.665 1_556 S
+O13 Si207 1.652 . S
+Cu14 O23 1.941 . S
+Cu14 O219 1.928 . S
+Cu14 Na187 3.199 . S
+Cu14 O258 1.928 . S
+Cu14 Na111 3.141 . S
+Cu14 O140 1.935 . S
+Cu14 Na72 3.220 . S
+Si15 O11 1.657 1_554 S
+Si15 O9 1.616 1_565 S
+Si15 O24 1.618 . S
+Si15 O27 1.583 . S
+Si16 O24 1.622 . S
+Si16 O30 1.584 . S
+Si17 O29 1.622 1_545 S
+Si17 O32 1.582 . S
+Si17 O236 1.658 . S
+Si18 O23 1.577 . S
+Si18 O26 1.620 . S
+Si19 O33 1.577 . S
+Si19 O34 1.624 . S
+Si19 O237 1.658 . S
+Si20 O25 1.579 . S
+Si20 O200 1.652 . S
+Si20 O142 1.618 . S
+Si21 O13 1.665 1_554 S
+Si21 O28 1.580 . S
+Si21 O71 1.611 . S
+Si22 O26 1.619 . S
+Si22 O31 1.580 . S
+Si22 O227 1.659 . S
+O23 Na72 2.583 . S
+O23 Na111 2.427 . S
+O25 Na36 2.470 . S
+O25 Cu317 1.937 . S
+O27 Cu127 1.935 1_554 S
+O27 Na73 2.430 . S
+O27 Na110 2.595 . S
+O28 Cu318 1.931 . S
+O28 Na73 2.480 1_545 S
+O29 Si17 1.622 1_565 S
+O29 Si133 1.618 . S
+O29 Na73 2.576 . S
+O30 Cu313 1.933 . S
+O30 Na110 2.578 . S
+O30 Na112 2.436 . S
+O31 Cu315 1.936 . S
+O31 Na72 2.583 . S
+O31 Na74 2.435 . S
+O32 Cu201 1.928 . S
+O32 Na74 2.488 . S
+O33 Cu239 1.928 . S
+O33 Na112 2.489 . S
+O34 Na37 2.564 . S
+O34 Si58 1.611 . S
+Na35 Na37 3.575 . S
+Na35 Cu51 3.220 . S
+Na35 O60 2.583 . S
+Na35 O102 2.595 . S
+Na35 O105 2.578 . S
+Na35 O68 2.583 . S
+Na35 Na149 3.581 . S
+Na35 Cu314 3.231 . S
+Na36 Cu317 3.217 . S
+Na36 Cu89 3.141 1_554 S
+Na36 O98 2.427 . S
+Na36 O64 2.430 . S
+Na36 O65 2.480 . S
+Na36 O66 2.576 . S
+Na36 Na148 3.581 . S
+Na37 Cu164 3.199 . S
+Na37 O105 2.436 . S
+Na37 O68 2.435 . S
+Na37 O69 2.488 . S
+Na37 O108 2.489 . S
+Na37 Cu314 3.123 . S
+Si38 O40 1.600 . S
+Si38 O44 1.606 . S
+Si38 O45 1.606 . S
+Si38 O46 1.602 . S
+O39 Si53 1.649 . S
+O39 Si285 1.659 1_554 S
+O40 Si59 1.623 . S
+O41 Si57 1.622 . S
+O41 Si304 1.607 1_655 S
+O42 Si56 1.626 . S
+O42 Si305 1.604 . S
+O43 Si53 1.621 . S
+O43 Si305 1.599 1_655 S
+O44 Si58 1.623 . S
+O45 Si54 1.627 . S
+O46 Si52 1.616 . S
+O47 Si55 1.619 . S
+O47 Si304 1.603 1_655 S
+O48 Si52 1.657 1_556 S
+O48 Si280 1.658 . S
+O49 Si55 1.656 . S
+O49 Si282 1.658 1_655 S
+O50 Si58 1.665 1_556 S
+O50 Si170 1.652 . S
+Cu51 O60 1.941 . S
+Cu51 O182 1.928 . S
+Cu51 Na224 3.199 . S
+Cu51 O296 1.928 . S
+Cu51 Na149 3.141 . S
+Cu51 O102 1.935 . S
+Si52 O48 1.657 1_554 S
+Si52 O61 1.618 . S
+Si52 O64 1.583 . S
+Si53 O61 1.622 . S
+Si53 O67 1.584 . S
+Si54 O66 1.622 . S
+Si54 O69 1.582 . S
+Si54 O274 1.658 . S
+Si55 O60 1.577 . S
+Si55 O63 1.620 . S
+Si56 O70 1.577 1_455 S
+Si56 O71 1.624 . S
+Si56 O275 1.658 1_455 S
+Si57 O62 1.579 . S
+Si57 O163 1.652 . S
+Si57 O104 1.618 . S
+Si58 O50 1.665 1_554 S
+Si58 O65 1.580 . S
+Si59 O63 1.619 . S
+Si59 O68 1.580 . S
+Si59 O265 1.659 . S
+O60 Na149 2.427 . S
+O62 Na73 2.470 1_655 S
+O62 Cu318 1.937 1_665 S
+O64 Cu89 1.935 1_554 S
+O64 Na148 2.595 . S
+O65 Cu317 1.931 . S
+O66 Si95 1.618 . S
+O67 Cu316 1.933 . S
+O67 Na148 2.578 . S
+O67 Na150 2.436 . S
+O68 Cu314 1.936 . S
+O69 Cu164 1.928 . S
+O70 Si56 1.577 1_655 S
+O70 Cu277 1.928 . S
+O70 Na150 2.489 . S
+O71 Na74 2.564 . S
+Na72 Na74 3.575 . S
+Na72 O140 2.595 . S
+Na72 O143 2.578 . S
+Na72 Na111 3.581 . S
+Na72 Cu315 3.231 . S
+Na73 O62 2.470 1_455 S
+Na73 Cu318 3.217 1_565 S
+Na73 Cu127 3.141 1_554 S
+Na73 O136 2.427 . S
+Na73 O28 2.480 1_565 S
+Na73 Na110 3.581 . S
+Na74 Cu201 3.199 . S
+Na74 O143 2.436 . S
+Na74 O146 2.489 . S
+Na74 Cu315 3.123 . S
+Si76 O78 1.600 . S
+Si76 O82 1.606 . S
+Si76 O83 1.606 . S
+Si76 O84 1.602 . S
+O77 Si91 1.649 . S
+O77 Si172 1.659 . S
+O78 Si97 1.623 . S
+O79 Si95 1.622 . S
+O79 Si301 1.607 . S
+O80 Si94 1.626 . S
+O80 Si302 1.604 . S
+O81 Si91 1.621 . S
+O81 Si302 1.599 . S
+O82 Si96 1.623 . S
+O83 Si92 1.627 . S
+O84 Si90 1.616 1_545 S
+O85 Si93 1.619 . S
+O85 Si301 1.603 . S
+O86 Si90 1.657 . S
+O86 Si167 1.658 . S
+O87 Si93 1.656 1_556 S
+O87 Si169 1.658 . S
+O88 Si96 1.665 . S
+O88 Si283 1.652 . S
+Cu89 O98 1.941 1_556 S
+Cu89 O295 1.928 . S
+Cu89 Na262 3.199 . S
+Cu89 O183 1.928 . S
+Cu89 Na36 3.141 1_556 S
+Cu89 O64 1.935 1_556 S
+Cu89 Na148 3.220 1_556 S
+Si90 O84 1.616 1_565 S
+Si90 O99 1.618 . S
+Si90 O102 1.583 . S
+Si91 O99 1.622 . S
+Si91 O105 1.584 . S
+Si92 O104 1.622 1_545 S
+Si92 O107 1.582 . S
+Si92 O161 1.658 . S
+Si93 O87 1.656 1_554 S
+Si93 O98 1.577 . S
+Si93 O101 1.620 . S
+Si94 O108 1.577 . S
+Si94 O109 1.624 . S
+Si94 O162 1.658 . S
+Si95 O100 1.579 . S
+Si95 O276 1.652 . S
+Si96 O103 1.580 . S
+Si96 O147 1.611 . S
+Si97 O101 1.619 . S
+Si97 O106 1.580 . S
+Si97 O152 1.659 1_554 S
+O98 Cu89 1.941 1_554 S
+O98 Na148 2.583 . S
+O100 Na111 2.470 . S
+O100 Cu319 1.937 . S
+O102 Na149 2.430 . S
+O103 Cu320 1.931 1_655 S
+O103 Na149 2.480 1_545 S
+O104 Si92 1.622 1_565 S
+O104 Na149 2.576 . S
+O105 Cu314 1.933 . S
+O106 Cu316 1.936 . S
+O106 Na148 2.583 . S
+O106 Na150 2.435 . S
+O107 Cu277 1.928 . S
+O107 Na150 2.488 . S
+O108 Cu164 1.928 . S
+O109 Na112 2.564 . S
+O109 Si134 1.611 . S
+Na110 Na112 3.575 . S
+Na110 Cu127 3.220 1_554 S
+Na110 O136 2.583 . S
+Na110 O144 2.583 . S
+Na110 Cu313 3.231 . S
+Na111 Cu319 3.217 . S
+Na111 O140 2.430 . S
+Na111 O141 2.480 . S
+Na111 O142 2.576 . S
+Na112 Cu239 3.199 . S
+Na112 O144 2.435 . S
+Na112 O145 2.488 . S
+Na112 Cu313 3.123 . S
+Si114 O116 1.600 . S
+Si114 O120 1.606 . S
+Si114 O121 1.606 . S
+Si114 O122 1.602 . S
+O115 Si129 1.649 . S
+O115 Si209 1.659 . S
+O116 Si135 1.623 . S
+O117 Si133 1.622 . S
+O117 Si304 1.607 . S
+O118 Si132 1.626 . S
+O118 Si305 1.604 1_655 S
+O119 Si129 1.621 . S
+O119 Si305 1.599 . S
+O120 Si134 1.623 . S
+O121 Si130 1.627 . S
+O122 Si128 1.616 . S
+O123 Si131 1.619 . S
+O123 Si304 1.603 . S
+O124 Si128 1.657 . S
+O124 Si204 1.658 . S
+O125 Si131 1.656 1_556 S
+O125 Si206 1.658 1_455 S
+O126 Si134 1.665 . S
+O126 Si245 1.652 . S
+Cu127 O136 1.941 1_556 S
+Cu127 O257 1.928 . S
+Cu127 Na300 3.199 . S
+Cu127 O220 1.928 . S
+Cu127 Na73 3.141 1_556 S
+Cu127 O27 1.935 1_556 S
+Cu127 Na110 3.220 1_556 S
+Si128 O137 1.618 . S
+Si128 O140 1.583 . S
+Si129 O137 1.622 . S
+Si129 O143 1.584 . S
+Si130 O142 1.622 . S
+Si130 O145 1.582 . S
+Si130 O198 1.658 . S
+Si131 O125 1.656 1_554 S
+Si131 O136 1.577 . S
+Si131 O139 1.620 . S
+Si132 O146 1.577 1_655 S
+Si132 O147 1.624 . S
+Si132 O199 1.658 1_655 S
+Si133 O138 1.579 . S
+Si133 O238 1.652 . S
+Si134 O141 1.580 . S
+Si135 O139 1.619 . S
+Si135 O144 1.580 . S
+Si135 O189 1.659 1_554 S
+O136 Cu127 1.941 1_554 S
+O138 Na149 2.470 1_455 S
+O138 Cu320 1.937 1_565 S
+O141 Cu319 1.931 . S
+O143 Cu315 1.933 . S
+O144 Cu313 1.936 . S
+O145 Cu239 1.928 . S
+O146 Si132 1.577 1_455 S
+O146 Cu201 1.928 . S
+O147 Na150 2.564 . S
+Na148 Na150 3.575 . S
+Na148 Cu89 3.220 1_554 S
+Na148 Cu316 3.231 . S
+Na149 O138 2.470 1_655 S
+Na149 Cu320 3.217 1_665 S
+Na149 O103 2.480 1_565 S
+Na150 Cu277 3.199 . S
+Na150 Cu316 3.123 . S
+Si151 O153 1.600 . S
+Si151 O157 1.606 . S
+Si151 O158 1.606 . S
+Si151 O159 1.602 . S
+O152 Si166 1.649 . S
+O152 Si97 1.659 1_556 S
+O153 Si172 1.623 . S
+O154 Si170 1.622 . S
+O154 Si307 1.607 . S
+O155 Si169 1.626 . S
+O155 Si308 1.604 . S
+O156 Si166 1.621 . S
+O156 Si308 1.599 . S
+O157 Si171 1.623 . S
+O158 Si167 1.627 . S
+O159 Si165 1.616 1_565 S
+O160 Si168 1.619 . S
+O160 Si307 1.603 . S
+O161 Si165 1.657 1_554 S
+O162 Si168 1.656 . S
+O163 Si171 1.665 1_554 S
+Cu164 O173 1.941 . S
+Cu164 Na261 3.141 . S
+Cu164 O290 1.935 . S
+Cu164 Na222 3.220 . S
+Si165 O161 1.657 1_556 S
+Si165 O159 1.616 1_545 S
+Si165 O174 1.618 . S
+Si165 O177 1.583 . S
+Si166 O174 1.622 . S
+Si166 O180 1.584 . S
+Si167 O179 1.622 1_565 S
+Si167 O182 1.582 . S
+Si168 O173 1.577 . S
+Si168 O176 1.620 . S
+Si169 O183 1.577 . S
+Si169 O184 1.624 . S
+Si170 O175 1.579 . S
+Si170 O292 1.618 . S
+Si171 O163 1.665 1_556 S
+Si171 O178 1.580 . S
+Si171 O221 1.611 . S
+Si172 O176 1.619 . S
+Si172 O181 1.580 . S
+O173 Na222 2.583 . S
+O173 Na261 2.427 . S
+O175 Na186 2.470 . S
+O175 Cu317 1.937 1_556 S
+O177 Cu277 1.935 1_556 S
+O177 Na223 2.430 . S
+O177 Na260 2.595 . S
+O178 Cu318 1.931 1_666 S
+O178 Na223 2.480 1_565 S
+O179 Si167 1.622 1_545 S
+O179 Si283 1.618 . S
+O179 Na223 2.576 . S
+O180 Cu316 1.933 1_556 S
+O180 Na260 2.578 . S
+O180 Na262 2.436 . S
+O181 Cu314 1.936 . S
+O181 Na222 2.583 . S
+O181 Na224 2.435 . S
+O182 Na224 2.488 . S
+O183 Na262 2.489 . S
+O184 Na187 2.564 . S
+O184 Si208 1.611 . S
+Na185 Na187 3.575 . S
+Na185 Cu201 3.220 . S
+Na185 O210 2.583 . S
+Na185 O252 2.595 . S
+Na185 O255 2.578 . S
+Na185 O218 2.583 . S
+Na185 Na299 3.581 . S
+Na185 Cu315 3.231 . S
+Na186 Cu317 3.217 1_556 S
+Na186 Cu239 3.141 1_556 S
+Na186 O248 2.427 . S
+Na186 O214 2.430 . S
+Na186 O215 2.480 . S
+Na186 O216 2.576 . S
+Na186 Na298 3.581 . S
+Na187 O255 2.436 . S
+Na187 O218 2.435 . S
+Na187 O219 2.488 . S
+Na187 O258 2.489 . S
+Na187 Cu315 3.123 . S
+Si188 O190 1.600 . S
+Si188 O194 1.606 . S
+Si188 O195 1.606 . S
+Si188 O196 1.602 . S
+O189 Si203 1.649 . S
+O189 Si135 1.659 1_556 S
+O190 Si209 1.623 . S
+O191 Si207 1.622 . S
+O191 Si310 1.607 . S
+O192 Si206 1.626 . S
+O192 Si311 1.604 1_655 S
+O193 Si203 1.621 . S
+O193 Si311 1.599 . S
+O194 Si208 1.623 . S
+O195 Si204 1.627 . S
+O196 Si202 1.616 . S
+O197 Si205 1.619 . S
+O197 Si310 1.603 . S
+O198 Si202 1.657 1_554 S
+O199 Si205 1.656 . S
+O199 Si132 1.658 1_455 S
+O200 Si208 1.665 1_554 S
+Cu201 O210 1.941 . S
+Cu201 Na299 3.141 . S
+Cu201 O252 1.935 . S
+Si202 O198 1.657 1_556 S
+Si202 O211 1.618 . S
+Si202 O214 1.583 . S
+Si203 O211 1.622 . S
+Si203 O217 1.584 . S
+Si204 O216 1.622 . S
+Si204 O219 1.582 . S
+Si205 O210 1.577 . S
+Si205 O213 1.620 . S
+Si206 O220 1.577 1_655 S
+Si206 O221 1.624 . S
+Si206 O125 1.658 1_655 S
+Si207 O212 1.579 . S
+Si207 O254 1.618 . S
+Si208 O200 1.665 1_556 S
+Si208 O215 1.580 . S
+Si209 O213 1.619 . S
+Si209 O218 1.580 . S
+O210 Na299 2.427 . S
+O212 Na223 2.470 1_455 S
+O212 Cu318 1.937 1_556 S
+O214 Cu239 1.935 1_556 S
+O214 Na298 2.595 . S
+O215 Cu317 1.931 1_556 S
+O216 Si245 1.618 . S
+O217 Cu313 1.933 1_556 S
+O217 Na298 2.578 . S
+O217 Na300 2.436 . S
+O218 Cu315 1.936 . S
+O220 Si206 1.577 1_455 S
+O220 Na300 2.489 . S
+O221 Na224 2.564 . S
+Na222 Na224 3.575 . S
+Na222 O290 2.595 . S
+Na222 O293 2.578 . S
+Na222 Na261 3.581 . S
+Na222 Cu314 3.231 . S
+Na223 O212 2.470 1_655 S
+Na223 Cu318 3.217 1_656 S
+Na223 Cu277 3.141 1_556 S
+Na223 O286 2.427 . S
+Na223 O178 2.480 1_545 S
+Na223 Na260 3.581 . S
+Na224 O293 2.436 . S
+Na224 O296 2.489 . S
+Na224 Cu314 3.123 . S
+Si226 O228 1.600 . S
+Si226 O232 1.606 . S
+Si226 O233 1.606 . S
+Si226 O234 1.602 . S
+O227 Si241 1.649 . S
+O228 Si247 1.623 . S
+O229 Si245 1.622 . S
+O229 Si307 1.607 . S
+O230 Si244 1.626 . S
+O230 Si308 1.604 . S
+O231 Si241 1.621 . S
+O231 Si308 1.599 . S
+O232 Si246 1.623 . S
+O233 Si242 1.627 . S
+O234 Si240 1.616 1_565 S
+O235 Si243 1.619 . S
+O235 Si307 1.603 . S
+O236 Si240 1.657 . S
+O237 Si243 1.656 1_554 S
+O238 Si246 1.665 . S
+Cu239 O248 1.941 1_554 S
+Cu239 Na186 3.141 1_554 S
+Cu239 O214 1.935 1_554 S
+Cu239 Na298 3.220 1_554 S
+Si240 O234 1.616 1_545 S
+Si240 O249 1.618 . S
+Si240 O252 1.583 . S
+Si241 O249 1.622 . S
+Si241 O255 1.584 . S
+Si242 O254 1.622 1_565 S
+Si242 O257 1.582 . S
+Si243 O237 1.656 1_556 S
+Si243 O248 1.577 . S
+Si243 O251 1.620 . S
+Si244 O258 1.577 . S
+Si244 O259 1.624 . S
+Si245 O250 1.579 . S
+Si246 O253 1.580 . S
+Si246 O297 1.611 . S
+Si247 O251 1.619 . S
+Si247 O256 1.580 . S
+Si247 O2 1.659 1_556 S
+O248 Cu239 1.941 1_556 S
+O248 Na298 2.583 . S
+O250 Na261 2.470 . S
+O250 Cu319 1.937 . S
+O252 Na299 2.430 . S
+O253 Cu320 1.931 1_565 S
+O253 Na299 2.480 1_565 S
+O254 Si242 1.622 1_545 S
+O254 Na299 2.576 . S
+O255 Cu315 1.933 . S
+O256 Cu313 1.936 1_556 S
+O256 Na298 2.583 . S
+O256 Na300 2.435 . S
+O257 Na300 2.488 . S
+O259 Na262 2.564 . S
+O259 Si284 1.611 . S
+Na260 Na262 3.575 . S
+Na260 Cu277 3.220 1_556 S
+Na260 O286 2.583 . S
+Na260 O294 2.583 . S
+Na260 Cu316 3.231 1_556 S
+Na261 Cu319 3.217 . S
+Na261 O290 2.430 . S
+Na261 O291 2.480 . S
+Na261 O292 2.576 . S
+Na262 O294 2.435 . S
+Na262 O295 2.488 . S
+Na262 Cu316 3.123 1_556 S
+Si264 O266 1.600 . S
+Si264 O270 1.606 . S
+Si264 O271 1.606 . S
+Si264 O272 1.602 . S
+O265 Si279 1.649 . S
+O266 Si285 1.623 . S
+O267 Si283 1.622 . S
+O267 Si310 1.607 1_655 S
+O268 Si282 1.626 . S
+O268 Si311 1.604 . S
+O269 Si279 1.621 . S
+O269 Si311 1.599 1_655 S
+O270 Si284 1.623 . S
+O271 Si280 1.627 . S
+O272 Si278 1.616 . S
+O273 Si281 1.619 . S
+O273 Si310 1.603 1_655 S
+O274 Si278 1.657 . S
+O275 Si281 1.656 1_554 S
+O275 Si56 1.658 1_655 S
+O276 Si284 1.665 . S
+Cu277 O286 1.941 1_554 S
+Cu277 Na223 3.141 1_554 S
+Cu277 O177 1.935 1_554 S
+Cu277 Na260 3.220 1_554 S
+Si278 O287 1.618 . S
+Si278 O290 1.583 . S
+Si279 O287 1.622 . S
+Si279 O293 1.584 . S
+Si280 O292 1.622 . S
+Si280 O295 1.582 . S
+Si281 O275 1.656 1_556 S
+Si281 O286 1.577 . S
+Si281 O289 1.620 . S
+Si282 O296 1.577 1_455 S
+Si282 O297 1.624 . S
+Si282 O49 1.658 1_455 S
+Si283 O288 1.579 . S
+Si284 O291 1.580 . S
+Si285 O289 1.619 . S
+Si285 O294 1.580 . S
+Si285 O39 1.659 1_556 S
+O286 Cu277 1.941 1_556 S
+O288 Na299 2.470 1_655 S
+O288 Cu320 1.937 1_655 S
+O291 Cu319 1.931 . S
+O293 Cu314 1.933 . S
+O294 Cu316 1.936 1_556 S
+O296 Si282 1.577 1_655 S
+O297 Na300 2.564 . S
+Na298 Na300 3.575 . S
+Na298 Cu239 3.220 1_556 S
+Na298 Cu313 3.231 1_556 S
+Na299 O288 2.470 1_455 S
+Na299 Cu320 3.217 . S
+Na299 O253 2.480 1_545 S
+Na300 Cu313 3.123 1_556 S
+Si304 O41 1.607 1_455 S
+Si304 O47 1.603 1_455 S
+Si305 O118 1.604 1_455 S
+Si305 O43 1.599 1_455 S
+Si310 O267 1.607 1_455 S
+Si310 O273 1.603 1_455 S
+Si311 O192 1.604 1_455 S
+Si311 O269 1.599 1_455 S
+Cu313 O217 1.933 1_554 S
+Cu313 O256 1.936 1_554 S
+Cu313 Na298 3.231 1_554 S
+Cu313 Na300 3.123 1_554 S
+Cu316 O180 1.933 1_554 S
+Cu316 O294 1.936 1_554 S
+Cu316 Na260 3.231 1_554 S
+Cu316 Na262 3.123 1_554 S
+Cu317 O175 1.937 1_554 S
+Cu317 Na186 3.217 1_554 S
+Cu317 O215 1.931 1_554 S
+Cu318 O62 1.937 1_445 S
+Cu318 O212 1.937 1_554 S
+Cu318 Na73 3.217 1_545 S
+Cu318 Na223 3.217 1_454 S
+Cu318 O178 1.931 1_444 S
+Cu320 O138 1.937 1_545 S
+Cu320 O288 1.937 1_455 S
+Cu320 Na149 3.217 1_445 S
+Cu320 O103 1.931 1_455 S
+Cu320 O253 1.931 1_545 S
diff --git a/benchmarks/mof/structures/dac/SIFSIX-18-Ni-beta.cif b/benchmarks/mof/structures/dac/SIFSIX-18-Ni-beta.cif
new file mode 100644
index 0000000000000000000000000000000000000000..8064cb6205bfad2d25c855ba32a89ec915d8e901
--- /dev/null
+++ b/benchmarks/mof/structures/dac/SIFSIX-18-Ni-beta.cif
@@ -0,0 +1,330 @@
+data_1888095
+_audit_creation_date 2025-01-20
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 14.1630
+_cell_length_b 13.8690
+_cell_length_c 7.4930
+_cell_angle_alpha 90.0000
+_cell_angle_beta 94.7800
+_cell_angle_gamma 90.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+F1 F 0.41747 0.41536 0.50000 0.01000 Uiso 1.00
+F2 F 0.50989 0.50002 0.73391 0.01000 Uiso 1.00
+F3 F 0.41747 0.58464 0.50000 0.01000 Uiso 1.00
+C4 C 0.68052 0.14651 0.63773 0.01000 Uiso 1.00
+H5 H 0.70881 0.16808 0.52958 0.01000 Uiso 1.00
+H6 H 0.62907 0.19030 0.66309 0.01000 Uiso 1.00
+H7 H 0.65572 0.08087 0.61944 0.01000 Uiso 1.00
+C8 C 0.75349 0.14693 0.79106 0.01000 Uiso 1.00
+C9 C 0.75910 0.19535 0.95517 0.01000 Uiso 1.00
+C10 C 0.68990 0.26364 0.01986 0.01000 Uiso 1.00
+C11 C 0.67841 0.36082 0.97210 0.01000 Uiso 1.00
+C12 C 0.73167 0.42231 0.85120 0.01000 Uiso 1.00
+H13 H 0.68691 0.45856 0.77008 0.01000 Uiso 1.00
+H14 H 0.77105 0.38119 0.78101 0.01000 Uiso 1.00
+H15 H 0.77202 0.46766 0.92291 0.01000 Uiso 1.00
+N16 N 0.83080 0.09220 0.78621 0.01000 Uiso 1.00
+H17 H 0.84388 0.05483 0.69639 0.01000 Uiso 1.00
+N18 N 0.60784 0.39589 0.06070 0.01000 Uiso 1.00
+C19 C 0.89121 0.19126 0.22266 0.01000 Uiso 1.00
+H20 H 0.90027 0.13294 0.29596 0.01000 Uiso 1.00
+H21 H 0.85176 0.23729 0.28233 0.01000 Uiso 1.00
+H22 H 0.95280 0.22070 0.20663 0.01000 Uiso 1.00
+C23 C 0.84413 0.16539 0.04519 0.01000 Uiso 1.00
+C24 C 0.62253 0.24419 0.14141 0.01000 Uiso 1.00
+C25 C 0.59980 0.15257 0.23376 0.01000 Uiso 1.00
+H26 H 0.59482 0.16551 0.36145 0.01000 Uiso 1.00
+H27 H 0.65011 0.10528 0.22019 0.01000 Uiso 1.00
+H28 H 0.53968 0.12683 0.18061 0.01000 Uiso 1.00
+N29 N 0.88530 0.10320 0.93930 0.01000 Uiso 1.00
+N30 N 0.57469 0.32598 0.16335 0.01000 Uiso 1.00
+H31 H 0.52867 0.33305 0.23421 0.01000 Uiso 1.00
+F32 F 0.91747 0.91536 0.50000 0.01000 Uiso 1.00
+F33 F 0.00989 0.00002 0.73391 0.01000 Uiso 1.00
+F34 F 0.91747 0.08464 0.50000 0.01000 Uiso 1.00
+C35 C 0.18052 0.64651 0.63773 0.01000 Uiso 1.00
+H36 H 0.20881 0.66808 0.52958 0.01000 Uiso 1.00
+H37 H 0.12907 0.69030 0.66309 0.01000 Uiso 1.00
+H38 H 0.15572 0.58087 0.61944 0.01000 Uiso 1.00
+C39 C 0.25349 0.64693 0.79106 0.01000 Uiso 1.00
+C40 C 0.25910 0.69535 0.95517 0.01000 Uiso 1.00
+C41 C 0.18990 0.76364 0.01986 0.01000 Uiso 1.00
+C42 C 0.17841 0.86082 0.97210 0.01000 Uiso 1.00
+C43 C 0.23167 0.92231 0.85120 0.01000 Uiso 1.00
+H44 H 0.18691 0.95856 0.77008 0.01000 Uiso 1.00
+H45 H 0.27105 0.88119 0.78101 0.01000 Uiso 1.00
+H46 H 0.27202 0.96766 0.92291 0.01000 Uiso 1.00
+N47 N 0.33080 0.59220 0.78621 0.01000 Uiso 1.00
+H48 H 0.34388 0.55483 0.69639 0.01000 Uiso 1.00
+N49 N 0.10784 0.89589 0.06070 0.01000 Uiso 1.00
+C50 C 0.39121 0.69126 0.22266 0.01000 Uiso 1.00
+H51 H 0.40027 0.63294 0.29596 0.01000 Uiso 1.00
+H52 H 0.35176 0.73729 0.28233 0.01000 Uiso 1.00
+H53 H 0.45280 0.72070 0.20663 0.01000 Uiso 1.00
+C54 C 0.34413 0.66539 0.04519 0.01000 Uiso 1.00
+C55 C 0.12253 0.74419 0.14141 0.01000 Uiso 1.00
+C56 C 0.09980 0.65257 0.23376 0.01000 Uiso 1.00
+H57 H 0.09482 0.66551 0.36145 0.01000 Uiso 1.00
+H58 H 0.15011 0.60528 0.22019 0.01000 Uiso 1.00
+H59 H 0.03968 0.62683 0.18061 0.01000 Uiso 1.00
+N60 N 0.38530 0.60320 0.93930 0.01000 Uiso 1.00
+N61 N 0.07469 0.82598 0.16335 0.01000 Uiso 1.00
+H62 H 0.02867 0.83305 0.23421 0.01000 Uiso 1.00
+F63 F 0.58253 0.41536 0.50000 0.01000 Uiso 1.00
+F64 F 0.49011 0.50002 0.26609 0.01000 Uiso 1.00
+F65 F 0.58253 0.58464 0.50000 0.01000 Uiso 1.00
+C66 C 0.31948 0.14651 0.36227 0.01000 Uiso 1.00
+H67 H 0.29119 0.16808 0.47042 0.01000 Uiso 1.00
+H68 H 0.37093 0.19030 0.33691 0.01000 Uiso 1.00
+H69 H 0.34428 0.08087 0.38056 0.01000 Uiso 1.00
+C70 C 0.24651 0.14693 0.20894 0.01000 Uiso 1.00
+C71 C 0.24090 0.19535 0.04483 0.01000 Uiso 1.00
+C72 C 0.31010 0.26364 0.98014 0.01000 Uiso 1.00
+C73 C 0.32159 0.36082 0.02790 0.01000 Uiso 1.00
+C74 C 0.26833 0.42231 0.14880 0.01000 Uiso 1.00
+H75 H 0.31309 0.45856 0.22992 0.01000 Uiso 1.00
+H76 H 0.22895 0.38119 0.21899 0.01000 Uiso 1.00
+H77 H 0.22798 0.46766 0.07709 0.01000 Uiso 1.00
+N78 N 0.16920 0.09220 0.21379 0.01000 Uiso 1.00
+H79 H 0.15612 0.05483 0.30361 0.01000 Uiso 1.00
+N80 N 0.39216 0.39589 0.93930 0.01000 Uiso 1.00
+C81 C 0.10879 0.19126 0.77734 0.01000 Uiso 1.00
+H82 H 0.09973 0.13294 0.70404 0.01000 Uiso 1.00
+H83 H 0.14824 0.23729 0.71767 0.01000 Uiso 1.00
+H84 H 0.04720 0.22070 0.79337 0.01000 Uiso 1.00
+C85 C 0.15587 0.16539 0.95481 0.01000 Uiso 1.00
+C86 C 0.37747 0.24419 0.85859 0.01000 Uiso 1.00
+C87 C 0.40020 0.15257 0.76624 0.01000 Uiso 1.00
+H88 H 0.40518 0.16551 0.63855 0.01000 Uiso 1.00
+H89 H 0.34989 0.10528 0.77981 0.01000 Uiso 1.00
+H90 H 0.46032 0.12683 0.81939 0.01000 Uiso 1.00
+N91 N 0.11470 0.10320 0.06070 0.01000 Uiso 1.00
+N92 N 0.42531 0.32598 0.83665 0.01000 Uiso 1.00
+H93 H 0.47133 0.33305 0.76579 0.01000 Uiso 1.00
+F94 F 0.08253 0.91536 0.50000 0.01000 Uiso 1.00
+F95 F 0.99011 0.00002 0.26609 0.01000 Uiso 1.00
+F96 F 0.08253 0.08464 0.50000 0.01000 Uiso 1.00
+C97 C 0.81948 0.64651 0.36227 0.01000 Uiso 1.00
+H98 H 0.79119 0.66808 0.47042 0.01000 Uiso 1.00
+H99 H 0.87093 0.69030 0.33691 0.01000 Uiso 1.00
+H100 H 0.84428 0.58087 0.38056 0.01000 Uiso 1.00
+C101 C 0.74651 0.64693 0.20894 0.01000 Uiso 1.00
+C102 C 0.74090 0.69535 0.04483 0.01000 Uiso 1.00
+C103 C 0.81010 0.76364 0.98014 0.01000 Uiso 1.00
+C104 C 0.82159 0.86082 0.02790 0.01000 Uiso 1.00
+C105 C 0.76833 0.92231 0.14880 0.01000 Uiso 1.00
+H106 H 0.81309 0.95856 0.22992 0.01000 Uiso 1.00
+H107 H 0.72895 0.88119 0.21899 0.01000 Uiso 1.00
+H108 H 0.72798 0.96766 0.07709 0.01000 Uiso 1.00
+N109 N 0.66920 0.59220 0.21379 0.01000 Uiso 1.00
+H110 H 0.65612 0.55483 0.30361 0.01000 Uiso 1.00
+N111 N 0.89216 0.89589 0.93930 0.01000 Uiso 1.00
+C112 C 0.60879 0.69126 0.77734 0.01000 Uiso 1.00
+H113 H 0.59973 0.63294 0.70404 0.01000 Uiso 1.00
+H114 H 0.64824 0.73729 0.71767 0.01000 Uiso 1.00
+H115 H 0.54720 0.72070 0.79337 0.01000 Uiso 1.00
+C116 C 0.65587 0.66539 0.95481 0.01000 Uiso 1.00
+C117 C 0.87747 0.74419 0.85859 0.01000 Uiso 1.00
+C118 C 0.90020 0.65257 0.76624 0.01000 Uiso 1.00
+H119 H 0.90518 0.66551 0.63855 0.01000 Uiso 1.00
+H120 H 0.84989 0.60528 0.77981 0.01000 Uiso 1.00
+H121 H 0.96032 0.62683 0.81939 0.01000 Uiso 1.00
+N122 N 0.61470 0.60320 0.06070 0.01000 Uiso 1.00
+N123 N 0.92531 0.82598 0.83665 0.01000 Uiso 1.00
+H124 H 0.97133 0.83305 0.76579 0.01000 Uiso 1.00
+Si125 Si 0.50000 0.50002 0.50000 0.01000 Uiso 1.00
+Si126 Si 0.00000 0.00002 0.50000 0.01000 Uiso 1.00
+Ni127 Ni 0.50000 0.50000 -0.00000 0.01000 Uiso 1.00
+Ni128 Ni 0.00000 0.00000 -0.00000 0.01000 Uiso 1.00
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_2
+_ccdc_geom_bond_type
+F1 Si125 1.657 . S
+F2 Si125 1.747 . S
+F2 Ni127 2.010 1_556 S
+F3 Si125 1.656 . S
+C4 H5 0.980 . S
+C4 H6 0.979 . S
+C4 H7 0.981 . S
+C4 C8 1.480 . S
+C8 C9 1.398 . D
+C8 N16 1.335 . S
+C9 C10 1.474 1_556 S
+C9 C23 1.394 1_556 S
+C10 C9 1.474 1_554 S
+C10 C11 1.400 1_554 S
+C10 C24 1.399 . D
+C11 C10 1.400 1_556 S
+C11 C12 1.494 . S
+C11 N18 1.337 1_556 D
+C12 H13 0.980 . S
+C12 H14 0.981 . S
+C12 H15 0.980 . S
+N16 H17 0.881 . S
+N16 N29 1.337 . S
+N18 C11 1.337 1_554 D
+N18 N30 1.346 . S
+N18 Ni127 2.123 . S
+C19 H20 0.980 . S
+C19 H21 0.980 . S
+C19 H22 0.980 . S
+C19 C23 1.481 . S
+C23 C9 1.394 1_554 S
+C23 N29 1.338 1_554 D
+C24 C25 1.495 . S
+C24 N30 1.338 . S
+C25 H26 0.982 . S
+C25 H27 0.980 . S
+C25 H28 0.977 . S
+N29 C23 1.338 1_556 D
+N29 Ni128 2.184 1_656 S
+N30 H31 0.880 . S
+F32 Si126 1.657 1_665 S
+F33 Si126 1.747 . S
+F33 Ni128 2.010 1_556 S
+F34 Si126 1.656 1_655 S
+C35 H36 0.980 . S
+C35 H37 0.979 . S
+C35 H38 0.981 . S
+C35 C39 1.480 . S
+C39 C40 1.398 . D
+C39 N47 1.335 . S
+C40 C41 1.474 1_556 S
+C40 C54 1.394 1_556 S
+C41 C40 1.474 1_554 S
+C41 C42 1.400 1_554 S
+C41 C55 1.399 . D
+C42 C41 1.400 1_556 S
+C42 C43 1.494 . S
+C42 N49 1.337 1_556 D
+C43 H44 0.980 . S
+C43 H45 0.981 . S
+C43 H46 0.980 . S
+N47 H48 0.881 . S
+N47 N60 1.337 . S
+N49 C42 1.337 1_554 D
+N49 N61 1.346 . S
+N49 Ni128 2.123 1_565 S
+C50 H51 0.980 . S
+C50 H52 0.980 . S
+C50 H53 0.980 . S
+C50 C54 1.481 . S
+C54 C40 1.394 1_554 S
+C54 N60 1.338 1_554 D
+C55 C56 1.495 . S
+C55 N61 1.338 . S
+C56 H57 0.982 . S
+C56 H58 0.980 . S
+C56 H59 0.977 . S
+N60 C54 1.338 1_556 D
+N60 Ni127 2.184 1_556 S
+N61 H62 0.880 . S
+F63 Si125 1.657 . S
+F64 Si125 1.747 . S
+F64 Ni127 2.010 . S
+F65 Si125 1.656 . S
+C66 H67 0.980 . S
+C66 H68 0.979 . S
+C66 H69 0.981 . S
+C66 C70 1.480 . S
+C70 C71 1.398 . D
+C70 N78 1.335 . S
+C71 C72 1.474 1_554 S
+C71 C85 1.394 1_554 S
+C72 C71 1.474 1_556 S
+C72 C73 1.400 1_556 S
+C72 C86 1.399 . D
+C73 C72 1.400 1_554 S
+C73 C74 1.494 . S
+C73 N80 1.337 1_554 D
+C74 H75 0.980 . S
+C74 H76 0.981 . S
+C74 H77 0.980 . S
+N78 H79 0.881 . S
+N78 N91 1.337 . S
+N80 C73 1.337 1_556 D
+N80 N92 1.346 . S
+N80 Ni127 2.123 1_556 S
+C81 H82 0.980 . S
+C81 H83 0.980 . S
+C81 H84 0.980 . S
+C81 C85 1.481 . S
+C85 C71 1.394 1_556 S
+C85 N91 1.338 1_556 D
+C86 C87 1.495 . S
+C86 N92 1.338 . S
+C87 H88 0.982 . S
+C87 H89 0.980 . S
+C87 H90 0.977 . S
+N91 C85 1.338 1_554 D
+N91 Ni128 2.184 . S
+N92 H93 0.880 . S
+F94 Si126 1.657 1_565 S
+F95 Si126 1.747 1_655 S
+F95 Ni128 2.010 1_655 S
+F96 Si126 1.656 . S
+C97 H98 0.980 . S
+C97 H99 0.979 . S
+C97 H100 0.981 . S
+C97 C101 1.480 . S
+C101 C102 1.398 . D
+C101 N109 1.335 . S
+C102 C103 1.474 1_554 S
+C102 C116 1.394 1_554 S
+C103 C102 1.474 1_556 S
+C103 C104 1.400 1_556 S
+C103 C117 1.399 . D
+C104 C103 1.400 1_554 S
+C104 C105 1.494 . S
+C104 N111 1.337 1_554 D
+C105 H106 0.980 . S
+C105 H107 0.981 . S
+C105 H108 0.980 . S
+N109 H110 0.881 . S
+N109 N122 1.337 . S
+N111 C104 1.337 1_556 D
+N111 N123 1.346 . S
+N111 Ni128 2.123 1_666 S
+C112 H113 0.980 . S
+C112 H114 0.980 . S
+C112 H115 0.980 . S
+C112 C116 1.481 . S
+C116 C102 1.394 1_556 S
+C116 N122 1.338 1_556 D
+C117 C118 1.495 . S
+C117 N123 1.338 . S
+C118 H119 0.982 . S
+C118 H120 0.980 . S
+C118 H121 0.977 . S
+N122 C116 1.338 1_554 D
+N122 Ni127 2.184 . S
+N123 H124 0.880 . S
+Si126 F32 1.657 1_445 S
+Si126 F94 1.657 1_545 S
+Si126 F95 1.747 1_455 S
+Si126 F34 1.656 1_455 S
+Ni127 N80 2.123 1_554 S
+Ni127 F2 2.010 1_554 S
+Ni127 N60 2.184 1_554 S
+Ni128 N49 2.123 1_545 S
+Ni128 N111 2.123 1_444 S
+Ni128 F33 2.010 1_554 S
+Ni128 F95 2.010 1_455 S
+Ni128 N29 2.184 1_454 S
diff --git a/benchmarks/mof/structures/dac/SIFSIX-3-Cu.cif b/benchmarks/mof/structures/dac/SIFSIX-3-Cu.cif
new file mode 100644
index 0000000000000000000000000000000000000000..9424b82a24a24891f93584c1e45b478ffe8443fd
--- /dev/null
+++ b/benchmarks/mof/structures/dac/SIFSIX-3-Cu.cif
@@ -0,0 +1,95 @@
+data_SIFSIX-3-Cu
+_audit_creation_date 2025-01-20
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 6.9186
+_cell_length_b 6.9186
+_cell_length_c 7.9061
+_cell_angle_alpha 90.0000
+_cell_angle_beta 90.0000
+_cell_angle_gamma 90.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+N1 N 0.50000 0.21700 0.50000 0.09900 Uiso 1.00
+N2 N 0.50000 0.78300 0.50000 0.09900 Uiso 1.00
+N3 N 0.78300 0.50000 0.50000 0.09900 Uiso 1.00
+N4 N 0.21700 0.50000 0.50000 0.09900 Uiso 1.00
+C5 C 0.50000 0.08930 0.38430 0.12100 Uiso 1.00
+H6 H 0.50000 0.14010 0.27980 0.16650 Uiso 1.00
+C7 C 0.50000 0.91070 0.38430 0.12100 Uiso 1.00
+H8 H 0.50000 0.85990 0.27980 0.16650 Uiso 1.00
+C9 C 0.91070 0.50000 0.38430 0.12100 Uiso 1.00
+H10 H 0.85990 0.50000 0.27980 0.16650 Uiso 1.00
+C11 C 0.08930 0.50000 0.38430 0.12100 Uiso 1.00
+H12 H 0.14010 0.50000 0.27980 0.16650 Uiso 1.00
+C13 C 0.50000 0.08930 0.61570 0.12100 Uiso 1.00
+H14 H 0.50000 0.14010 0.72020 0.16650 Uiso 1.00
+C15 C 0.50000 0.91070 0.61570 0.12100 Uiso 1.00
+H16 H 0.50000 0.85990 0.72020 0.16650 Uiso 1.00
+C17 C 0.08930 0.50000 0.61570 0.12100 Uiso 1.00
+H18 H 0.14010 0.50000 0.72020 0.16650 Uiso 1.00
+C19 C 0.91070 0.50000 0.61570 0.12100 Uiso 1.00
+H20 H 0.85990 0.50000 0.72020 0.16650 Uiso 1.00
+Cu21 Cu 0.50000 0.50000 0.50000 0.09800 Uiso 1.00
+F22 F 0.50000 0.50000 0.23200 0.11900 Uiso 1.00
+F23 F 0.50000 0.50000 0.76800 0.11900 Uiso 1.00
+F24 F 0.33130 0.33130 0.00000 0.20400 Uiso 1.00
+F25 F 0.66870 0.66870 0.00000 0.20400 Uiso 1.00
+F26 F 0.66870 0.33130 0.00000 0.20400 Uiso 1.00
+F27 F 0.33130 0.66870 0.00000 0.20400 Uiso 1.00
+Si28 Si 0.50000 0.50000 0.00000 0.15800 Uiso 1.00
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_2
+_ccdc_geom_bond_type
+N1 Cu21 1.958 . S
+N1 C5 1.272 . S
+N1 C13 1.272 . S
+N2 Cu21 1.958 . S
+N2 C7 1.272 . S
+N2 C15 1.272 . S
+N3 Cu21 1.958 . S
+N3 C9 1.272 . S
+N3 C19 1.272 . S
+N4 Cu21 1.958 . S
+N4 C11 1.272 . S
+N4 C17 1.272 . S
+C5 H6 0.898 . S
+C5 C7 1.236 1_545 S
+C7 H8 0.898 . S
+C7 C5 1.236 1_565 S
+C9 H10 0.898 . S
+C9 C11 1.236 1_655 S
+C11 H12 0.898 . S
+C11 C9 1.236 1_455 S
+C13 H14 0.898 . S
+C13 C15 1.236 1_545 S
+C15 H16 0.898 . S
+C15 C13 1.236 1_565 S
+C17 H18 0.898 . S
+C17 C19 1.236 1_455 S
+C19 H20 0.898 . S
+C19 C17 1.236 1_655 S
+Cu21 F22 2.119 . S
+Cu21 F23 2.119 . S
+F22 Si28 1.834 . S
+F23 Si28 1.834 1_556 S
+F24 Si28 1.651 . S
+F25 Si28 1.651 . S
+F26 Si28 1.651 . S
+F27 Si28 1.651 . S
+Si28 F23 1.834 1_554 S
diff --git a/benchmarks/mof/structures/dac/TIFSIX-3-Ni.cif b/benchmarks/mof/structures/dac/TIFSIX-3-Ni.cif
new file mode 100644
index 0000000000000000000000000000000000000000..d32e9bd37e2680036b0f0b41649a289d12c3f7d9
--- /dev/null
+++ b/benchmarks/mof/structures/dac/TIFSIX-3-Ni.cif
@@ -0,0 +1,95 @@
+data_1517365
+_audit_creation_date 2024-01-26
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 7.0012
+_cell_length_b 7.0012
+_cell_length_c 7.4979
+_cell_angle_alpha 90.0000
+_cell_angle_beta 90.0000
+_cell_angle_gamma 90.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+C1 C 0.90026 0.49844 0.65431 0.03390 Uiso 1.00
+H2 H 0.81423 0.49723 0.77818 0.04060 Uiso 1.00
+C3 C 0.50124 0.90015 0.65433 0.03390 Uiso 1.00
+H4 H 0.50222 0.81403 0.77815 0.04060 Uiso 1.00
+C5 C 0.90056 0.50154 0.34578 0.03390 Uiso 1.00
+H6 H 0.81477 0.50278 0.22176 0.04060 Uiso 1.00
+C7 C 0.49876 0.90067 0.34580 0.03390 Uiso 1.00
+H8 H 0.49779 0.81497 0.22172 0.04060 Uiso 1.00
+C9 C 0.09955 0.50035 0.34574 0.03390 Uiso 1.00
+H10 H 0.18543 0.50060 0.22177 0.04060 Uiso 1.00
+C11 C 0.49897 0.09954 0.34575 0.03390 Uiso 1.00
+H12 H 0.49795 0.18541 0.22177 0.04060 Uiso 1.00
+C13 C 0.09963 0.49965 0.65428 0.03390 Uiso 1.00
+H14 H 0.18557 0.49940 0.77821 0.04060 Uiso 1.00
+C15 C 0.50118 0.09964 0.65428 0.03390 Uiso 1.00
+H16 H 0.50190 0.18559 0.77820 0.04060 Uiso 1.00
+Ni17 Ni 0.50000 0.50000 0.50000 0.01300 Uiso 1.00
+Ti18 Ti 0.50000 0.50000 0.00000 0.03680 Uiso 1.00
+F19 F 0.50000 0.50000 0.76510 0.02310 Uiso 1.00
+F20 F 0.50000 0.50000 0.23490 0.02310 Uiso 1.00
+F21 F 0.66555 0.66555 0.00000 0.05180 Uiso 1.00
+F22 F 0.33445 0.33445 0.00000 0.05180 Uiso 1.00
+F23 F 0.33445 0.66555 0.00000 0.05180 Uiso 1.00
+F24 F 0.66555 0.33445 0.00000 0.05180 Uiso 1.00
+N25 N 0.80390 0.50001 0.49996 0.01620 Uiso 1.00
+N26 N 0.19610 0.50000 0.50000 0.01620 Uiso 1.00
+N27 N 0.50000 0.80390 0.50000 0.01620 Uiso 1.00
+N28 N 0.50000 0.19610 0.50000 0.01620 Uiso 1.00
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_2
+_ccdc_geom_bond_type
+C1 N25 1.340 . S
+C1 H2 1.107 . S
+C1 C13 1.396 1_655 S
+C3 N27 1.339 . S
+C3 H4 1.107 . S
+C3 C15 1.397 1_565 S
+C5 N25 1.340 . S
+C5 H6 1.107 . S
+C5 C9 1.393 1_655 S
+C7 N27 1.340 . S
+C7 H8 1.107 . S
+C7 C11 1.392 1_565 S
+C9 N26 1.340 . S
+C9 H10 1.107 . S
+C9 C5 1.393 1_455 S
+C11 N28 1.340 . S
+C11 H12 1.107 . S
+C11 C7 1.392 1_545 S
+C13 N26 1.340 . S
+C13 H14 1.107 . S
+C13 C1 1.396 1_455 S
+C15 N28 1.339 . S
+C15 H16 1.107 . S
+C15 C3 1.397 1_545 S
+Ni17 F19 1.988 . S
+Ni17 F20 1.988 . S
+Ni17 N25 2.128 . S
+Ni17 N26 2.128 . S
+Ni17 N27 2.128 . S
+Ni17 N28 2.128 . S
+Ti18 F19 1.761 1_554 S
+Ti18 F20 1.761 . S
+Ti18 F21 1.639 . S
+Ti18 F22 1.639 . S
+Ti18 F23 1.639 . S
+Ti18 F24 1.639 . S
+F19 Si18 1.761 1_556 S
diff --git a/benchmarks/mof/structures/dac/en-Mg2(dobpdc).cif b/benchmarks/mof/structures/dac/en-Mg2(dobpdc).cif
new file mode 100644
index 0000000000000000000000000000000000000000..4470ea3df77d2ec207e5152475ac417957132270
--- /dev/null
+++ b/benchmarks/mof/structures/dac/en-Mg2(dobpdc).cif
@@ -0,0 +1,492 @@
+data_image0
+_cell_length_a 36.6982
+_cell_length_b 36.6982
+_cell_length_c 6.93828
+_cell_angle_alpha 90
+_cell_angle_beta 90
+_cell_angle_gamma 120
+
+_symmetry_space_group_name_H-M "P 1"
+_symmetry_int_tables_number 1
+
+loop_
+ _symmetry_equiv_pos_as_xyz
+ 'x, y, z'
+
+loop_
+ _atom_site_label
+ _atom_site_occupancy
+ _atom_site_fract_x
+ _atom_site_fract_y
+ _atom_site_fract_z
+ _atom_site_thermal_displace_type
+ _atom_site_B_iso_or_equiv
+ _atom_site_type_symbol
+ C1 1.0000 0.01357 0.58780 0.71593 Biso 1.000 C
+ C2 1.0000 0.50638 0.98341 0.74715 Biso 1.000 C
+ C3 1.0000 0.65006 0.81037 0.58606 Biso 1.000 C
+ C4 1.0000 0.18963 0.83969 0.58606 Biso 1.000 C
+ C5 1.0000 0.16031 0.34994 0.58606 Biso 1.000 C
+ C6 1.0000 0.68813 0.89017 0.97288 Biso 1.000 C
+ C7 1.0000 0.10983 0.79796 0.97288 Biso 1.000 C
+ C8 1.0000 0.20204 0.31187 0.97287 Biso 1.000 C
+ C9 1.0000 0.47703 0.49362 0.74715 Biso 1.000 C
+ C10 1.0000 0.31187 0.10983 0.02712 Biso 1.000 C
+ C11 1.0000 0.79796 0.68813 0.02713 Biso 1.000 C
+ C12 1.0000 0.35482 0.22350 0.30643 Biso 1.000 C
+ C13 1.0000 0.77650 0.13131 0.30643 Biso 1.000 C
+ C14 1.0000 0.86869 0.64518 0.30643 Biso 1.000 C
+ C15 1.0000 0.97852 0.44314 0.36028 Biso 1.000 C
+ C16 1.0000 0.55686 0.53538 0.36028 Biso 1.000 C
+ C17 1.0000 0.46462 0.02148 0.36028 Biso 1.000 C
+ C18 1.0000 0.89017 0.20204 0.02712 Biso 1.000 C
+ C19 1.0000 0.01659 0.52297 0.74715 Biso 1.000 C
+ C20 1.0000 0.49362 0.01659 0.25285 Biso 1.000 C
+ C21 1.0000 0.52297 0.50638 0.25285 Biso 1.000 C
+ C22 1.0000 0.48498 0.00329 0.05850 Biso 1.000 C
+ C23 1.0000 0.00329 0.51831 0.94150 Biso 1.000 C
+ C24 1.0000 0.48169 0.48498 0.94150 Biso 1.000 C
+ C25 1.0000 0.51502 0.99671 0.94150 Biso 1.000 C
+ C26 1.0000 0.66336 0.81501 0.39171 Biso 1.000 C
+ C27 1.0000 0.18499 0.84835 0.39171 Biso 1.000 C
+ C28 1.0000 0.15165 0.33664 0.39171 Biso 1.000 C
+ C29 1.0000 0.68324 0.85628 0.08033 Biso 1.000 C
+ C30 1.0000 0.14372 0.82696 0.08033 Biso 1.000 C
+ C31 1.0000 0.17304 0.31676 0.08033 Biso 1.000 C
+ C32 1.0000 0.31676 0.14372 0.91967 Biso 1.000 C
+ C33 1.0000 0.85628 0.17304 0.91967 Biso 1.000 C
+ C34 1.0000 0.82696 0.68324 0.91967 Biso 1.000 C
+ C35 1.0000 0.34994 0.18963 0.41394 Biso 1.000 C
+ C36 1.0000 0.81037 0.16031 0.41394 Biso 1.000 C
+ C37 1.0000 0.83969 0.65006 0.41394 Biso 1.000 C
+ C38 1.0000 0.98341 0.47703 0.25285 Biso 1.000 C
+ C39 1.0000 0.02148 0.55686 0.63972 Biso 1.000 C
+ C40 1.0000 0.51831 0.51502 0.05850 Biso 1.000 C
+ C41 1.0000 0.44314 0.46462 0.63972 Biso 1.000 C
+ C42 1.0000 0.64518 0.77650 0.69357 Biso 1.000 C
+ C43 1.0000 0.77310 0.78457 0.42273 Biso 1.000 C
+ C44 1.0000 0.21543 0.98853 0.42273 Biso 1.000 C
+ C45 1.0000 0.29734 0.46199 0.19013 Biso 1.000 C
+ C46 1.0000 0.53801 0.83535 0.19013 Biso 1.000 C
+ C47 1.0000 0.16465 0.70266 0.19013 Biso 1.000 C
+ C48 1.0000 0.70266 0.53801 0.80987 Biso 1.000 C
+ C49 1.0000 0.46199 0.16465 0.80986 Biso 1.000 C
+ C50 1.0000 0.01147 0.22690 0.42273 Biso 1.000 C
+ C51 1.0000 0.83535 0.29734 0.80987 Biso 1.000 C
+ C52 1.0000 0.20468 0.16868 0.52353 Biso 1.000 C
+ C53 1.0000 0.83132 0.03600 0.52352 Biso 1.000 C
+ C54 1.0000 0.36932 0.87133 0.14310 Biso 1.000 C
+ C55 1.0000 0.12867 0.49799 0.14310 Biso 1.000 C
+ C56 1.0000 0.50202 0.63068 0.14310 Biso 1.000 C
+ C57 1.0000 0.63068 0.12867 0.85690 Biso 1.000 C
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+ Mg9 1.0000 0.03488 0.35906 0.13941 Biso 1.000 Mg
+ Mg10 1.0000 0.32418 0.96512 0.13941 Biso 1.000 Mg
+ Mg11 1.0000 0.64094 0.67582 0.13941 Biso 1.000 Mg
+ Mg12 1.0000 0.63180 0.97428 0.19390 Biso 1.000 Mg
+ Mg13 1.0000 0.34248 0.36820 0.19390 Biso 1.000 Mg
+ Mg14 1.0000 0.02572 0.65752 0.19390 Biso 1.000 Mg
+ Mg15 1.0000 0.36820 0.02572 0.80610 Biso 1.000 Mg
+ Mg16 1.0000 0.65752 0.63180 0.80610 Biso 1.000 Mg
+ Mg17 1.0000 0.69239 0.99085 0.52720 Biso 1.000 Mg
+ Mg18 1.0000 0.97428 0.34248 0.80610 Biso 1.000 Mg
+ N1 1.0000 0.95429 0.81467 0.68512 Biso 1.000 N
+ N2 1.0000 0.80628 0.28762 0.64827 Biso 1.000 N
+ N3 1.0000 0.48134 0.19372 0.64827 Biso 1.000 N
+ N4 1.0000 0.28762 0.48134 0.35173 Biso 1.000 N
+ N5 1.0000 0.19372 0.71238 0.35173 Biso 1.000 N
+ N6 1.0000 0.51866 0.80628 0.35173 Biso 1.000 N
+ N7 1.0000 0.71238 0.51866 0.64827 Biso 1.000 N
+ N8 1.0000 0.24481 0.99503 0.58316 Biso 1.000 N
+ N9 1.0000 0.47294 0.62097 0.98149 Biso 1.000 N
+ N10 1.0000 0.86039 0.04571 0.68512 Biso 1.000 N
+ N11 1.0000 0.32838 0.41689 0.08351 Biso 1.000 N
+ N12 1.0000 0.58311 0.91149 0.08351 Biso 1.000 N
+ N13 1.0000 0.08851 0.67162 0.08351 Biso 1.000 N
+ N14 1.0000 0.67162 0.58311 0.91649 Biso 1.000 N
+ N15 1.0000 0.41689 0.08851 0.91649 Biso 1.000 N
+ N16 1.0000 0.91149 0.32838 0.91649 Biso 1.000 N
+ N17 1.0000 0.99503 0.75022 0.41684 Biso 1.000 N
+ N18 1.0000 0.24978 0.24481 0.41684 Biso 1.000 N
+ N19 1.0000 0.75519 0.00497 0.41684 Biso 1.000 N
+ N20 1.0000 0.33829 0.91644 0.24977 Biso 1.000 N
+ N21 1.0000 0.08356 0.42186 0.24977 Biso 1.000 N
+ N22 1.0000 0.18533 0.13961 0.68512 Biso 1.000 N
+ N23 1.0000 0.57814 0.66171 0.24977 Biso 1.000 N
+ N24 1.0000 0.91644 0.57814 0.75023 Biso 1.000 N
+ N25 1.0000 0.42186 0.33829 0.75023 Biso 1.000 N
+ N26 1.0000 0.00497 0.24978 0.58316 Biso 1.000 N
+ N27 1.0000 0.13961 0.95429 0.31488 Biso 1.000 N
+ N28 1.0000 0.81467 0.86039 0.31488 Biso 1.000 N
+ N29 1.0000 0.04571 0.18533 0.31488 Biso 1.000 N
+ N30 1.0000 0.52706 0.37903 0.01851 Biso 1.000 N
+ N31 1.0000 0.85197 0.47294 0.01851 Biso 1.000 N
+ N32 1.0000 0.62097 0.14803 0.01851 Biso 1.000 N
+ N33 1.0000 0.14803 0.52706 0.98149 Biso 1.000 N
+ N34 1.0000 0.37903 0.85197 0.98149 Biso 1.000 N
+ N35 1.0000 0.66171 0.08356 0.75023 Biso 1.000 N
+ N36 1.0000 0.75022 0.75519 0.58316 Biso 1.000 N
+ O1 1.0000 0.95572 0.25935 0.25478 Biso 1.000 O
+ O2 1.0000 0.28901 0.92601 0.92138 Biso 1.000 O
+ O3 1.0000 0.98890 0.34951 0.34709 Biso 1.000 O
+ O4 1.0000 0.65050 0.63939 0.34709 Biso 1.000 O
+ O5 1.0000 0.36061 0.01110 0.34709 Biso 1.000 O
+ O6 1.0000 0.67778 0.98382 0.98619 Biso 1.000 O
+ O7 1.0000 0.01618 0.69396 0.98619 Biso 1.000 O
+ O8 1.0000 0.34109 0.05549 0.66578 Biso 1.000 O
+ O9 1.0000 0.94451 0.28560 0.66578 Biso 1.000 O
+ O10 1.0000 0.71440 0.65891 0.66578 Biso 1.000 O
+ O11 1.0000 0.65891 0.94451 0.33422 Biso 1.000 O
+ O12 1.0000 0.05549 0.71440 0.33422 Biso 1.000 O
+ O13 1.0000 0.28560 0.34109 0.33422 Biso 1.000 O
+ O14 1.0000 0.00778 0.38883 0.99897 Biso 1.000 O
+ O15 1.0000 0.61117 0.61895 0.99897 Biso 1.000 O
+ O16 1.0000 0.38105 0.99222 0.99897 Biso 1.000 O
+ O17 1.0000 0.32557 0.27784 0.66765 Biso 1.000 O
+ O18 1.0000 0.72216 0.04773 0.66765 Biso 1.000 O
+ O19 1.0000 0.95227 0.67442 0.66765 Biso 1.000 O
+ O20 1.0000 0.67443 0.72216 0.33235 Biso 1.000 O
+ O21 1.0000 0.27784 0.95227 0.33235 Biso 1.000 O
+ O22 1.0000 0.04773 0.32558 0.33235 Biso 1.000 O
+ O23 1.0000 0.99222 0.61117 0.00103 Biso 1.000 O
+ O24 1.0000 0.38883 0.38105 0.00103 Biso 1.000 O
+ O25 1.0000 0.61895 0.00778 0.00103 Biso 1.000 O
+ O26 1.0000 0.63939 0.98890 0.65291 Biso 1.000 O
+ O27 1.0000 0.63700 0.71099 0.92138 Biso 1.000 O
+ O28 1.0000 0.34950 0.36061 0.65291 Biso 1.000 O
+ O29 1.0000 0.69396 0.67778 0.01381 Biso 1.000 O
+ O30 1.0000 0.07399 0.36300 0.92138 Biso 1.000 O
+ O31 1.0000 0.36300 0.28901 0.07862 Biso 1.000 O
+ O32 1.0000 0.71099 0.07399 0.07862 Biso 1.000 O
+ O33 1.0000 0.92601 0.63700 0.07862 Biso 1.000 O
+ O34 1.0000 0.30363 0.04428 0.25477 Biso 1.000 O
+ O35 1.0000 0.74065 0.69637 0.25478 Biso 1.000 O
+ O36 1.0000 0.02966 0.62236 0.41201 Biso 1.000 O
+ O37 1.0000 0.37764 0.40730 0.41201 Biso 1.000 O
+ O38 1.0000 0.59270 0.97034 0.41201 Biso 1.000 O
+ O39 1.0000 0.97034 0.37764 0.58799 Biso 1.000 O
+ O40 1.0000 0.62236 0.59270 0.58799 Biso 1.000 O
+ O41 1.0000 0.40730 0.02966 0.58799 Biso 1.000 O
+ O42 1.0000 0.69637 0.95572 0.74523 Biso 1.000 O
+ O43 1.0000 0.04428 0.74065 0.74522 Biso 1.000 O
+ O44 1.0000 0.25935 0.30363 0.74522 Biso 1.000 O
+ O45 1.0000 0.65556 0.68285 0.68047 Biso 1.000 O
+ O46 1.0000 0.31715 0.97271 0.68047 Biso 1.000 O
+ O47 1.0000 0.02729 0.34444 0.68047 Biso 1.000 O
+ O48 1.0000 0.34444 0.31715 0.31953 Biso 1.000 O
+ O49 1.0000 0.68285 0.02729 0.31953 Biso 1.000 O
+ O50 1.0000 0.97271 0.65556 0.31953 Biso 1.000 O
+ O51 1.0000 0.32222 0.01618 0.01381 Biso 1.000 O
+ O52 1.0000 0.98382 0.30604 0.01381 Biso 1.000 O
+ O53 1.0000 0.01110 0.65049 0.65291 Biso 1.000 O
+ O54 1.0000 0.30604 0.32222 0.98619 Biso 1.000 O
diff --git a/benchmarks/mof/structures/flue_gas/Al-PyrMOF.cif b/benchmarks/mof/structures/flue_gas/Al-PyrMOF.cif
new file mode 100644
index 0000000000000000000000000000000000000000..60b9a8538959bd6fe9bf4dc74c3e61cd29368b86
--- /dev/null
+++ b/benchmarks/mof/structures/flue_gas/Al-PyrMOF.cif
@@ -0,0 +1,185 @@
+data_image0
+_chemical_formula_structural OC4OC4OC4OC5O2COCOCOC5OC4OC4OC4OCOCOCOAl4OHOHOHOHC2HC5HC5HC5HC5HC5HC5HC5HC5HC2HC2HC2H33
+_chemical_formula_sum "O20 C88 Al4 H48"
+_cell_length_a 30.779
+_cell_length_b 6.6323
+_cell_length_c 15.589
+_cell_angle_alpha 90
+_cell_angle_beta 90
+_cell_angle_gamma 90
+
+_space_group_name_H-M_alt "P 1"
+_space_group_IT_number 1
+
+loop_
+ _space_group_symop_operation_xyz
+ 'x, y, z'
+
+loop_
+ _atom_site_type_symbol
+ _atom_site_label
+ _atom_site_symmetry_multiplicity
+ _atom_site_fract_x
+ _atom_site_fract_y
+ _atom_site_fract_z
+ _atom_site_occupancy
+ O O1 1.0 0.20590 0.34840 0.09560 1.0000
+ C C1 1.0 0.17159 0.39543 0.26965 1.0000
+ C C2 1.0 0.14561 0.38868 0.34163 1.0000
+ C C3 1.0 0.09895 0.61376 0.27998 1.0000
+ C C4 1.0 0.12493 0.62051 0.20801 1.0000
+ O O2 1.0 0.70590 0.84840 0.09560 1.0000
+ C C5 1.0 0.67159 0.89543 0.26965 1.0000
+ C C6 1.0 0.64561 0.88868 0.34163 1.0000
+ C C7 1.0 0.59895 0.11376 0.27998 1.0000
+ C C8 1.0 0.62493 0.12051 0.20801 1.0000
+ O O3 1.0 0.79410 0.65160 0.09560 1.0000
+ C C9 1.0 0.82841 0.60457 0.26965 1.0000
+ C C10 1.0 0.85439 0.61132 0.34163 1.0000
+ C C11 1.0 0.90105 0.38624 0.27998 1.0000
+ C C12 1.0 0.87507 0.37949 0.20801 1.0000
+ O O4 1.0 0.29410 0.15160 0.09560 1.0000
+ C C13 1.0 0.33875 0.98866 0.20284 1.0000
+ C C14 1.0 0.32841 0.10457 0.26965 1.0000
+ C C15 1.0 0.35439 0.11132 0.34163 1.0000
+ C C16 1.0 0.40105 0.88624 0.27998 1.0000
+ C C17 1.0 0.37507 0.87949 0.20801 1.0000
+ O O5 1.0 0.79410 0.34840 0.90440 1.0000
+ O O6 1.0 0.29410 0.84840 0.90440 1.0000
+ C C18 1.0 0.33875 0.01134 0.79716 1.0000
+ O O7 1.0 0.20590 0.65160 0.90440 1.0000
+ C C19 1.0 0.16125 0.48866 0.79716 1.0000
+ O O8 1.0 0.70590 0.15160 0.90440 1.0000
+ C C20 1.0 0.66125 0.98866 0.79716 1.0000
+ O O9 1.0 0.79410 0.65160 0.90440 1.0000
+ C C21 1.0 0.83875 0.48866 0.79716 1.0000
+ C C22 1.0 0.82841 0.60457 0.73035 1.0000
+ C C23 1.0 0.85439 0.61132 0.65837 1.0000
+ C C24 1.0 0.90105 0.38624 0.72002 1.0000
+ C C25 1.0 0.87507 0.37949 0.79199 1.0000
+ O O10 1.0 0.29410 0.15160 0.90440 1.0000
+ C C26 1.0 0.32841 0.10457 0.73035 1.0000
+ C C27 1.0 0.35439 0.11132 0.65837 1.0000
+ C C28 1.0 0.40105 0.88624 0.72002 1.0000
+ C C29 1.0 0.37507 0.87949 0.79199 1.0000
+ O O11 1.0 0.20590 0.34840 0.90440 1.0000
+ C C30 1.0 0.17159 0.39543 0.73035 1.0000
+ C C31 1.0 0.14561 0.38868 0.65837 1.0000
+ C C32 1.0 0.09895 0.61376 0.72002 1.0000
+ C C33 1.0 0.12493 0.62051 0.79199 1.0000
+ O O12 1.0 0.70590 0.84840 0.90440 1.0000
+ C C34 1.0 0.67159 0.89543 0.73035 1.0000
+ C C35 1.0 0.64561 0.88868 0.65837 1.0000
+ C C36 1.0 0.59895 0.11376 0.72002 1.0000
+ C C37 1.0 0.62493 0.12051 0.79199 1.0000
+ O O13 1.0 0.20590 0.65160 0.09560 1.0000
+ C C38 1.0 0.16125 0.48866 0.20284 1.0000
+ O O14 1.0 0.70590 0.15160 0.09560 1.0000
+ C C39 1.0 0.66125 0.98866 0.20284 1.0000
+ O O15 1.0 0.79410 0.34840 0.09560 1.0000
+ C C40 1.0 0.83875 0.51134 0.20284 1.0000
+ O O16 1.0 0.29410 0.84840 0.09560 1.0000
+ Al Al1 1.0 0.25000 0.25000 0.00000 1.0000
+ Al Al2 1.0 0.75000 0.75000 0.00000 1.0000
+ Al Al3 1.0 0.75000 0.25000 0.00000 1.0000
+ Al Al4 1.0 0.25000 0.75000 0.00000 1.0000
+ O O17 1.0 0.28370 0.50000 0.00000 1.0000
+ H H1 1.0 0.31560 0.50000 0.00000 1.0000
+ O O18 1.0 0.78370 0.00000 0.00000 1.0000
+ H H2 1.0 0.81560 0.00000 0.00000 1.0000
+ O O19 1.0 0.71630 0.50000 0.00000 1.0000
+ H H3 1.0 0.68440 0.50000 0.00000 1.0000
+ O O20 1.0 0.21630 0.00000 0.00000 1.0000
+ H H4 1.0 0.18440 0.00000 0.00000 1.0000
+ C C41 1.0 0.19440 0.50000 0.12230 1.0000
+ C C42 1.0 0.02490 0.50000 0.33930 1.0000
+ H H5 1.0 0.04160 0.50000 0.28920 1.0000
+ C C43 1.0 0.09010 0.50000 0.42570 1.0000
+ C C44 1.0 0.04410 0.50000 0.42130 1.0000
+ C C45 1.0 0.10929 0.50000 0.34679 1.0000
+ C C46 1.0 0.69440 0.00000 0.12230 1.0000
+ C C47 1.0 0.52490 0.00000 0.33930 1.0000
+ H H6 1.0 0.54160 0.00000 0.28920 1.0000
+ C C48 1.0 0.59010 0.00000 0.42570 1.0000
+ C C49 1.0 0.54410 0.00000 0.42130 1.0000
+ C C50 1.0 0.60929 0.00000 0.34679 1.0000
+ C C51 1.0 0.80560 0.50000 0.12230 1.0000
+ C C52 1.0 0.97510 0.50000 0.33930 1.0000
+ H H7 1.0 0.95840 0.50000 0.28920 1.0000
+ C C53 1.0 0.90990 0.50000 0.42570 1.0000
+ C C54 1.0 0.95590 0.50000 0.42130 1.0000
+ C C55 1.0 0.89071 0.50000 0.34679 1.0000
+ C C56 1.0 0.30560 0.00000 0.12230 1.0000
+ C C57 1.0 0.47510 0.00000 0.33930 1.0000
+ H H8 1.0 0.45840 0.00000 0.28920 1.0000
+ C C58 1.0 0.40990 0.00000 0.42570 1.0000
+ C C59 1.0 0.45590 0.00000 0.42130 1.0000
+ C C60 1.0 0.39071 0.00000 0.34679 1.0000
+ C C61 1.0 0.80560 0.50000 0.87770 1.0000
+ C C62 1.0 0.97510 0.50000 0.66070 1.0000
+ H H9 1.0 0.95840 0.50000 0.71080 1.0000
+ C C63 1.0 0.90990 0.50000 0.57430 1.0000
+ C C64 1.0 0.95590 0.50000 0.57870 1.0000
+ C C65 1.0 0.89071 0.50000 0.65321 1.0000
+ C C66 1.0 0.30560 0.00000 0.87770 1.0000
+ C C67 1.0 0.47510 0.00000 0.66070 1.0000
+ H H10 1.0 0.45840 0.00000 0.71080 1.0000
+ C C68 1.0 0.40990 0.00000 0.57430 1.0000
+ C C69 1.0 0.45590 0.00000 0.57870 1.0000
+ C C70 1.0 0.39071 0.00000 0.65321 1.0000
+ C C71 1.0 0.19440 0.50000 0.87770 1.0000
+ C C72 1.0 0.02490 0.50000 0.66070 1.0000
+ H H11 1.0 0.04160 0.50000 0.71080 1.0000
+ C C73 1.0 0.09010 0.50000 0.57430 1.0000
+ C C74 1.0 0.04410 0.50000 0.57870 1.0000
+ C C75 1.0 0.10929 0.50000 0.65321 1.0000
+ C C76 1.0 0.69440 0.00000 0.87770 1.0000
+ C C77 1.0 0.52490 0.00000 0.66070 1.0000
+ H H12 1.0 0.54160 0.00000 0.71080 1.0000
+ C C78 1.0 0.59010 0.00000 0.57430 1.0000
+ C C79 1.0 0.54410 0.00000 0.57870 1.0000
+ C C80 1.0 0.60929 0.00000 0.65321 1.0000
+ C C81 1.0 0.02240 0.50000 0.50000 1.0000
+ C C82 1.0 0.11390 0.50000 0.50000 1.0000
+ H H13 1.0 0.14480 0.50000 0.50000 1.0000
+ C C83 1.0 0.52240 0.00000 0.50000 1.0000
+ C C84 1.0 0.61390 0.00000 0.50000 1.0000
+ H H14 1.0 0.64480 0.00000 0.50000 1.0000
+ C C85 1.0 0.97760 0.50000 0.50000 1.0000
+ C C86 1.0 0.88610 0.50000 0.50000 1.0000
+ H H15 1.0 0.85520 0.50000 0.50000 1.0000
+ C C87 1.0 0.47760 0.00000 0.50000 1.0000
+ C C88 1.0 0.38610 0.00000 0.50000 1.0000
+ H H16 1.0 0.35520 0.00000 0.50000 1.0000
+ H H17 1.0 0.70417 0.81367 0.26919 1.0000
+ H H18 1.0 0.65486 0.78752 0.39786 1.0000
+ H H19 1.0 0.56789 0.20721 0.28279 1.0000
+ H H20 1.0 0.61712 0.23049 0.15396 1.0000
+ H H21 1.0 0.20417 0.31367 0.26919 1.0000
+ H H22 1.0 0.15486 0.28752 0.39786 1.0000
+ H H23 1.0 0.06789 0.70721 0.28279 1.0000
+ H H24 1.0 0.11712 0.73049 0.15396 1.0000
+ H H25 1.0 0.79583 0.68633 0.26919 1.0000
+ H H26 1.0 0.84514 0.71248 0.39786 1.0000
+ H H27 1.0 0.93211 0.29279 0.28279 1.0000
+ H H28 1.0 0.88288 0.26951 0.15396 1.0000
+ H H29 1.0 0.29765 0.20023 0.26786 1.0000
+ H H30 1.0 0.34514 0.21248 0.39786 1.0000
+ H H31 1.0 0.43211 0.79279 0.28279 1.0000
+ H H32 1.0 0.38509 0.77836 0.15229 1.0000
+ H H33 1.0 0.79765 0.70023 0.73214 1.0000
+ H H34 1.0 0.84514 0.71248 0.60214 1.0000
+ H H35 1.0 0.93211 0.29279 0.71721 1.0000
+ H H36 1.0 0.88509 0.27836 0.84771 1.0000
+ H H37 1.0 0.29583 0.18633 0.73081 1.0000
+ H H38 1.0 0.34514 0.21248 0.60214 1.0000
+ H H39 1.0 0.43211 0.79279 0.71721 1.0000
+ H H40 1.0 0.38288 0.76951 0.84604 1.0000
+ H H41 1.0 0.20417 0.31367 0.73081 1.0000
+ H H42 1.0 0.15486 0.28752 0.60214 1.0000
+ H H43 1.0 0.06789 0.70721 0.71721 1.0000
+ H H44 1.0 0.11712 0.73049 0.84604 1.0000
+ H H45 1.0 0.70417 0.81367 0.73081 1.0000
+ H H46 1.0 0.65486 0.78752 0.60214 1.0000
+ H H47 1.0 0.56789 0.20721 0.71721 1.0000
+ H H48 1.0 0.61712 0.23049 0.84604 1.0000
diff --git a/benchmarks/mof/structures/flue_gas/CALF20.cif b/benchmarks/mof/structures/flue_gas/CALF20.cif
new file mode 100644
index 0000000000000000000000000000000000000000..a8cd85afe688a75219deeefab1c92e6649f848f5
--- /dev/null
+++ b/benchmarks/mof/structures/flue_gas/CALF20.cif
@@ -0,0 +1,79 @@
+data_CALF20
+_audit_creation_date 2025-01-03
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P21/C'
+_symmetry_Int_Tables_number 14
+_symmetry_cell_setting monoclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+ -x,y+1/2,-z+1/2
+ -x,-y,-z
+ x,-y+1/2,z+1/2
+_cell_length_a 8.9138
+_cell_length_b 9.6935
+_cell_length_c 9.4836
+_cell_angle_alpha 90.0000
+_cell_angle_beta 115.8950
+_cell_angle_gamma 90.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+Zn1 Zn 0.17588 0.05771 0.43679 0.01878 Uani 1.00
+N1 N 0.03080 -0.11080 0.36830 0.02235 Uani 1.00
+N2 N -0.09220 -0.14750 0.41000 0.02454 Uani 1.00
+N3 N -0.09920 -0.29140 0.22590 0.02563 Uani 1.00
+O1 O 0.40980 0.07610 0.61020 0.03235 Uani 1.00
+O2 O 0.67530 0.03070 0.67320 0.02972 Uani 1.00
+C1 C 0.02150 -0.19830 0.25880 0.02987 Uani 1.00
+H1A H 0.09320 -0.19550 0.20860 0.03600 Uiso 1.00
+C2 C -0.16550 -0.25540 0.32320 0.02964 Uani 1.00
+H2A H -0.25590 -0.30290 0.32890 0.03600 Uiso 1.00
+C3 C 0.52480 0.03080 0.58150 0.02323 Uani 1.00
+loop_
+_atom_site_aniso_label
+_atom_site_aniso_U_11
+_atom_site_aniso_U_22
+_atom_site_aniso_U_33
+_atom_site_aniso_U_12
+_atom_site_aniso_U_13
+_atom_site_aniso_U_23
+Zn1 0.01910 0.01960 0.02120 -0.00037 0.01210 0.00021
+N1 0.02510 0.02230 0.02770 -0.00420 0.01900 -0.00430
+N2 0.02610 0.02640 0.02940 -0.00570 0.01980 -0.00540
+N3 0.02870 0.02630 0.02900 -0.00580 0.01920 -0.00810
+O1 0.01990 0.04770 0.03200 0.00370 0.01370 -0.00870
+O2 0.02170 0.03940 0.02870 0.00180 0.01160 -0.00580
+C1 0.03180 0.03110 0.03860 -0.01080 0.02640 -0.01070
+C2 0.03200 0.03190 0.03800 -0.01230 0.02730 -0.01110
+C3 0.01980 0.02350 0.02720 0.00070 0.01100 0.00160
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_2
+_ccdc_geom_bond_type
+Zn1 N1 2.007 . S
+Zn1 O1 2.022 . S
+Zn1 N3 2.016 2 S
+Zn1 N2 2.091 3_556 S
+Zn1 O2 2.189 3_656 S
+N1 C1 1.315 . S
+N1 N2 1.365 . S
+N2 C2 1.315 . S
+N2 Zn1 2.091 3_556 S
+N3 C1 1.333 . S
+N3 C2 1.341 . S
+N3 Zn1 2.016 2_545 S
+O1 C3 1.250 . S
+O2 C3 1.240 . S
+O2 Zn1 2.189 3_656 S
+C1 H1A 0.950 . S
+C2 H2A 0.950 . S
+C3 C3 1.531 3_656 S
diff --git a/benchmarks/mof/structures/flue_gas/MIL-120.cif b/benchmarks/mof/structures/flue_gas/MIL-120.cif
new file mode 100644
index 0000000000000000000000000000000000000000..21eedbae0ed0f1bb90b66e8d264f8cbb672b6ae2
--- /dev/null
+++ b/benchmarks/mof/structures/flue_gas/MIL-120.cif
@@ -0,0 +1,64 @@
+data_BUSQIQ_clean
+_audit_creation_date 2014-07-02
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diff --git a/benchmarks/mof/structures/flue_gas/MIL-96(Al).cif b/benchmarks/mof/structures/flue_gas/MIL-96(Al).cif
new file mode 100644
index 0000000000000000000000000000000000000000..49b82762d6755cb6aa5dc3cfe2b6ce16d412db6d
--- /dev/null
+++ b/benchmarks/mof/structures/flue_gas/MIL-96(Al).cif
@@ -0,0 +1,1120 @@
+data_cm7b03203_si_003
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+loop_
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+O3 Al241 1.869 1_565 S
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+C4 C297 1.384 . D
+C4 C6 1.421 . S
+C4 H5 0.950 1_565 S
+H5 C4 0.950 1_545 S
+C6 C298 1.360 . D
+C6 C7 1.456 . S
+C7 O2 1.280 1_565 D
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+H9 O232 1.110 . S
+H10 O289 1.110 . S
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+O11 C304 1.260 . D
+O12 C17 1.280 1_655 D
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+O13 Al242 1.869 . S
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+C14 C16 1.421 . S
+C14 H15 0.950 1_455 S
+H15 C14 0.950 1_655 S
+C16 C302 1.360 . D
+C16 C17 1.456 . S
+C17 O12 1.280 1_455 D
+H18 O251 1.110 . S
+H19 O290 1.110 . S
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+O21 C26 1.280 . D
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+O22 Al243 1.869 . S
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+C23 C25 1.421 . S
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+C45 C46 1.456 . S
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+H94 O234 1.110 . S
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+Al229 O86 1.870 1_655 S
+Al229 O142 1.870 1_545 S
+Al229 O171 1.870 1_655 S
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+Al231 O11 1.870 1_655 S
+Al231 O152 1.870 1_655 S
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+Al233 O95 1.870 1_545 S
+Al233 O180 1.870 1_545 S
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+Al235 O57 1.870 1_455 S
+Al235 O114 1.870 1_565 S
+Al235 O199 1.870 1_455 S
+Al235 O236 1.850 . S
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+Al237 O40 1.870 1_455 S
+Al237 O125 1.870 1_455 S
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+Al239 O68 1.870 1_565 S
+Al239 O210 1.870 1_565 S
+Al239 O240 1.850 . S
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+Al241 O269 1.842 . S
+Al241 O3 1.869 1_545 S
+Al241 O182 1.869 1_545 S
+Al242 O251 1.842 . S
+Al242 O266 1.842 1_455 S
+Al242 O59 1.869 1_455 S
+Al242 O127 1.869 1_455 S
+Al243 O254 1.842 . S
+Al243 O272 1.842 . S
+Al244 O257 1.842 1_554 S
+Al244 O278 1.842 1_545 S
+Al244 O97 1.869 1_544 S
+Al244 O144 1.869 1_544 S
+Al245 O260 1.842 1_454 S
+Al245 O275 1.842 . S
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+Al245 O154 1.869 1_554 S
+Al245 O201 1.869 1_455 S
+Al246 O263 1.842 1_554 S
+Al246 O281 1.842 . S
+Al246 O106 1.869 1_554 S
+Al246 O163 1.869 1_554 S
+Al247 O285 1.954 . S
+Al247 O287 1.954 . S
+Al247 O248 1.871 . S
+Al247 O249 1.850 . S
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+Al250 O285 1.954 . S
+Al250 O251 1.871 . S
+Al250 O252 1.942 . S
+Al253 O287 1.954 . S
+Al253 O286 1.954 . S
+Al253 O254 1.871 . S
+Al253 O255 1.942 . S
+Al256 O288 1.954 . S
+Al256 O290 1.954 . S
+Al256 O257 1.871 . S
+Al256 O258 1.850 . S
+O257 Al244 1.842 1_556 S
+Al259 O289 1.954 . S
+Al259 O288 1.954 . S
+Al259 O260 1.871 . S
+Al259 O261 1.850 . S
+O260 Al245 1.842 1_656 S
+Al262 O290 1.954 . S
+Al262 O289 1.954 . S
+Al262 O263 1.871 . S
+Al262 O264 1.850 . S
+O263 Al246 1.842 1_556 S
+Al265 O291 1.954 . S
+Al265 O293 1.954 . S
+Al265 O266 1.871 . S
+Al265 O267 1.850 . S
+O266 Al242 1.842 1_655 S
+Al268 O292 1.954 . S
+Al268 O291 1.954 . S
+Al268 O269 1.871 . S
+Al268 O270 1.850 . S
+Al271 O293 1.954 . S
+Al271 O292 1.954 . S
+Al271 O272 1.871 . S
+Al271 O273 1.850 . S
+Al274 O294 1.954 . S
+Al274 O296 1.954 . S
+Al274 O275 1.871 . S
+Al274 O276 1.942 . S
+Al277 O295 1.954 . S
+Al277 O294 1.954 . S
+Al277 O278 1.871 . S
+Al277 O279 1.850 . S
+O278 Al244 1.842 1_565 S
+Al280 O296 1.954 . S
+Al280 O295 1.954 . S
+Al280 O281 1.871 . S
+Al280 O282 1.942 . S
+O283 Al229 1.831 1_455 S
+O283 Al231 1.831 1_445 S
+O284 Al235 1.831 1_545 S
+O284 Al239 1.831 1_445 S
+C297 C300 1.499 . S
+C298 H299 0.949 . S
+C301 C304 1.499 . S
+C302 H303 0.949 . S
+C305 C308 1.499 . S
+C306 H307 0.949 . S
+C309 C312 1.499 . S
+C310 H311 0.949 . S
+C313 C316 1.499 . S
+C314 H315 0.949 . S
+C317 C320 1.499 . S
+C318 H319 0.949 . S
+C321 C324 1.499 . S
+C322 H323 0.949 . S
+C325 C328 1.499 . S
+C326 H327 0.949 . S
+C329 C332 1.499 . S
+C330 H331 0.949 . S
+C333 C336 1.499 . S
+C334 H335 0.949 . S
+C337 C340 1.499 . S
+C338 H339 0.949 . S
+C341 C344 1.499 . S
+C342 H343 0.949 . S
diff --git a/benchmarks/mof/structures/flue_gas/MUF-16.cif b/benchmarks/mof/structures/flue_gas/MUF-16.cif
new file mode 100644
index 0000000000000000000000000000000000000000..625c01482f199920536f7844e358b43cf9e54b1b
--- /dev/null
+++ b/benchmarks/mof/structures/flue_gas/MUF-16.cif
@@ -0,0 +1,382 @@
+data_MUF16(Mn)\guest\free\NPD\structure
+_audit_creation_date 2025-01-20
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 15.4733
+_cell_length_b 4.5111
+_cell_length_c 25.4716
+_cell_angle_alpha 90.0000
+_cell_angle_beta 97.0740
+_cell_angle_gamma 90.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+C1 C 0.42660 0.00200 0.12870 0.03812 Uiso 1.00
+C2 C 0.43100 0.20500 0.17330 0.03812 Uiso 1.00
+C3 C 0.50360 0.36900 0.18630 0.03812 Uiso 1.00
+C4 C 0.58420 0.31500 0.16290 0.03812 Uiso 1.00
+C5 C 0.58150 0.13000 0.11990 0.03812 Uiso 1.00
+C6 C 0.49880 0.98400 0.10410 0.03812 Uiso 1.00
+C7 C 0.35040 0.22300 0.20050 0.03812 Uiso 1.00
+C8 C 0.49870 0.76200 0.05810 0.03812 Uiso 1.00
+N9 N 0.66090 0.45600 0.18040 0.03812 Uiso 1.00
+O10 O 0.35220 0.46000 0.23250 0.03812 Uiso 1.00
+O11 O 0.29200 0.05300 0.19010 0.03812 Uiso 1.00
+O12 O 0.42140 0.63500 0.04110 0.03812 Uiso 1.00
+O13 O 0.56460 0.72400 0.03710 0.03812 Uiso 1.00
+H14 H 0.63590 0.08100 0.10590 0.03812 Uiso 1.00
+H15 H 0.49580 0.51800 0.21510 0.03812 Uiso 1.00
+H16 H 0.37140 0.85200 0.11700 0.03812 Uiso 1.00
+H17 H 0.64650 0.69200 0.19730 0.03812 Uiso 1.00
+H18 H 0.70140 0.47100 0.15320 0.03812 Uiso 1.00
+H19 H 0.43390 0.51200 0.01120 0.03812 Uiso 1.00
+C20 C 0.92660 0.50200 0.62870 0.03812 Uiso 1.00
+C21 C 0.93100 0.70500 0.67330 0.03812 Uiso 1.00
+C22 C 1.00360 0.86900 0.68630 0.03812 Uiso 1.00
+C23 C 1.08420 0.81500 0.66290 0.03812 Uiso 1.00
+C24 C 1.08150 0.63000 0.61990 0.03812 Uiso 1.00
+C25 C 0.99880 0.48400 0.60410 0.03812 Uiso 1.00
+C26 C 0.85040 0.72300 0.70050 0.03812 Uiso 1.00
+C27 C 0.99870 0.26200 0.55810 0.03812 Uiso 1.00
+N28 N 1.16090 0.95600 0.68040 0.03812 Uiso 1.00
+O29 O 0.85220 0.96000 0.73250 0.03812 Uiso 1.00
+O30 O 0.79200 0.55300 0.69010 0.03812 Uiso 1.00
+O31 O 0.92140 0.13500 0.54110 0.03812 Uiso 1.00
+O32 O 1.06460 0.22400 0.53710 0.03812 Uiso 1.00
+H33 H 1.13590 0.58100 0.60590 0.03812 Uiso 1.00
+H34 H 0.99580 0.01800 0.71510 0.03812 Uiso 1.00
+H35 H 0.87140 0.35200 0.61700 0.03812 Uiso 1.00
+H36 H 1.14650 0.19200 0.69730 0.03812 Uiso 1.00
+H37 H 1.20140 0.97100 0.65320 0.03812 Uiso 1.00
+H38 H 0.93390 0.01200 0.51120 0.03812 Uiso 1.00
+C39 C 0.07340 0.00200 0.87130 0.03812 Uiso 1.00
+C40 C 0.06900 0.20500 0.82670 0.03812 Uiso 1.00
+C41 C -0.00360 0.36900 0.81370 0.03812 Uiso 1.00
+C42 C -0.08420 0.31500 0.83710 0.03812 Uiso 1.00
+C43 C -0.08150 0.13000 0.88010 0.03812 Uiso 1.00
+C44 C 0.00120 0.98400 0.89590 0.03812 Uiso 1.00
+C45 C 0.14960 0.22300 0.79950 0.03812 Uiso 1.00
+C46 C 0.00130 0.76200 0.94190 0.03812 Uiso 1.00
+N47 N -0.16090 0.45600 0.81960 0.03812 Uiso 1.00
+O48 O 0.14780 0.46000 0.76750 0.03812 Uiso 1.00
+O49 O 0.20800 0.05300 0.80990 0.03812 Uiso 1.00
+O50 O 0.07860 0.63500 0.95890 0.03812 Uiso 1.00
+O51 O -0.06460 0.72400 0.96290 0.03812 Uiso 1.00
+H52 H -0.13590 0.08100 0.89410 0.03812 Uiso 1.00
+H53 H 0.00420 0.51800 0.78490 0.03812 Uiso 1.00
+H54 H 0.12860 0.85200 0.88300 0.03812 Uiso 1.00
+H55 H -0.14650 0.69200 0.80270 0.03812 Uiso 1.00
+H56 H -0.20140 0.47100 0.84680 0.03812 Uiso 1.00
+H57 H 0.06610 0.51200 0.98880 0.03812 Uiso 1.00
+C58 C 0.57340 0.50200 0.37130 0.03812 Uiso 1.00
+C59 C 0.56900 0.70500 0.32670 0.03812 Uiso 1.00
+C60 C 0.49640 0.86900 0.31370 0.03812 Uiso 1.00
+C61 C 0.41580 0.81500 0.33710 0.03812 Uiso 1.00
+C62 C 0.41850 0.63000 0.38010 0.03812 Uiso 1.00
+C63 C 0.50120 0.48400 0.39590 0.03812 Uiso 1.00
+C64 C 0.64960 0.72300 0.29950 0.03812 Uiso 1.00
+C65 C 0.50130 0.26200 0.44190 0.03812 Uiso 1.00
+N66 N 0.33910 0.95600 0.31960 0.03812 Uiso 1.00
+O67 O 0.64780 0.96000 0.26750 0.03812 Uiso 1.00
+O68 O 0.70800 0.55300 0.30990 0.03812 Uiso 1.00
+O69 O 0.57860 0.13500 0.45890 0.03812 Uiso 1.00
+O70 O 0.43540 0.22400 0.46290 0.03812 Uiso 1.00
+H71 H 0.36410 0.58100 0.39410 0.03812 Uiso 1.00
+H72 H 0.50420 0.01800 0.28490 0.03812 Uiso 1.00
+H73 H 0.62860 0.35200 0.38300 0.03812 Uiso 1.00
+H74 H 0.35350 0.19200 0.30270 0.03812 Uiso 1.00
+H75 H 0.29860 0.97100 0.34680 0.03812 Uiso 1.00
+H76 H 0.56610 0.01200 0.48880 0.03812 Uiso 1.00
+C77 C 0.57340 0.99800 0.87130 0.03812 Uiso 1.00
+C78 C 0.56900 0.79500 0.82670 0.03812 Uiso 1.00
+C79 C 0.49640 0.63100 0.81370 0.03812 Uiso 1.00
+C80 C 0.41580 0.68500 0.83710 0.03812 Uiso 1.00
+C81 C 0.41850 0.87000 0.88010 0.03812 Uiso 1.00
+C82 C 0.50120 0.01600 0.89590 0.03812 Uiso 1.00
+C83 C 0.64960 0.77700 0.79950 0.03812 Uiso 1.00
+C84 C 0.50130 0.23800 0.94190 0.03812 Uiso 1.00
+N85 N 0.33910 0.54400 0.81960 0.03812 Uiso 1.00
+O86 O 0.64780 0.54000 0.76750 0.03812 Uiso 1.00
+O87 O 0.70800 0.94700 0.80990 0.03812 Uiso 1.00
+O88 O 0.57860 0.36500 0.95890 0.03812 Uiso 1.00
+O89 O 0.43540 0.27600 0.96290 0.03812 Uiso 1.00
+H90 H 0.36410 0.91900 0.89410 0.03812 Uiso 1.00
+H91 H 0.50420 0.48200 0.78490 0.03812 Uiso 1.00
+H92 H 0.62860 0.14800 0.88300 0.03812 Uiso 1.00
+H93 H 0.35350 0.30800 0.80270 0.03812 Uiso 1.00
+H94 H 0.29860 0.52900 0.84680 0.03812 Uiso 1.00
+H95 H 0.56610 0.48800 0.98880 0.03812 Uiso 1.00
+C96 C 0.07340 0.49800 0.37130 0.03812 Uiso 1.00
+C97 C 0.06900 0.29500 0.32670 0.03812 Uiso 1.00
+C98 C -0.00360 0.13100 0.31370 0.03812 Uiso 1.00
+C99 C -0.08420 0.18500 0.33710 0.03812 Uiso 1.00
+C100 C -0.08150 0.37000 0.38010 0.03812 Uiso 1.00
+C101 C 0.00120 0.51600 0.39590 0.03812 Uiso 1.00
+C102 C 0.14960 0.27700 0.29950 0.03812 Uiso 1.00
+C103 C 0.00130 0.73800 0.44190 0.03812 Uiso 1.00
+N104 N -0.16090 0.04400 0.31960 0.03812 Uiso 1.00
+O105 O 0.14780 0.04000 0.26750 0.03812 Uiso 1.00
+O106 O 0.20800 0.44700 0.30990 0.03812 Uiso 1.00
+O107 O 0.07860 0.86500 0.45890 0.03812 Uiso 1.00
+O108 O -0.06460 0.77600 0.46290 0.03812 Uiso 1.00
+H109 H -0.13590 0.41900 0.39410 0.03812 Uiso 1.00
+H110 H 0.00420 0.98200 0.28490 0.03812 Uiso 1.00
+H111 H 0.12860 0.64800 0.38300 0.03812 Uiso 1.00
+H112 H -0.14650 0.80800 0.30270 0.03812 Uiso 1.00
+H113 H -0.20140 0.02900 0.34680 0.03812 Uiso 1.00
+H114 H 0.06610 0.98800 0.48880 0.03812 Uiso 1.00
+C115 C 0.92660 0.99800 0.12870 0.03812 Uiso 1.00
+C116 C 0.93100 0.79500 0.17330 0.03812 Uiso 1.00
+C117 C 1.00360 0.63100 0.18630 0.03812 Uiso 1.00
+C118 C 1.08420 0.68500 0.16290 0.03812 Uiso 1.00
+C119 C 1.08150 0.87000 0.11990 0.03812 Uiso 1.00
+C120 C 0.99880 0.01600 0.10410 0.03812 Uiso 1.00
+C121 C 0.85040 0.77700 0.20050 0.03812 Uiso 1.00
+C122 C 0.99870 0.23800 0.05810 0.03812 Uiso 1.00
+N123 N 1.16090 0.54400 0.18040 0.03812 Uiso 1.00
+O124 O 0.85220 0.54000 0.23250 0.03812 Uiso 1.00
+O125 O 0.79200 0.94700 0.19010 0.03812 Uiso 1.00
+O126 O 0.92140 0.36500 0.04110 0.03812 Uiso 1.00
+O127 O 1.06460 0.27600 0.03710 0.03812 Uiso 1.00
+H128 H 1.13590 0.91900 0.10590 0.03812 Uiso 1.00
+H129 H 0.99580 0.48200 0.21510 0.03812 Uiso 1.00
+H130 H 0.87140 0.14800 0.11700 0.03812 Uiso 1.00
+H131 H 1.14650 0.30800 0.19730 0.03812 Uiso 1.00
+H132 H 1.20140 0.52900 0.15320 0.03812 Uiso 1.00
+H133 H 0.93390 0.48800 0.01120 0.03812 Uiso 1.00
+C134 C 0.42660 0.49800 0.62870 0.03812 Uiso 1.00
+C135 C 0.43100 0.29500 0.67330 0.03812 Uiso 1.00
+C136 C 0.50360 0.13100 0.68630 0.03812 Uiso 1.00
+C137 C 0.58420 0.18500 0.66290 0.03812 Uiso 1.00
+C138 C 0.58150 0.37000 0.61990 0.03812 Uiso 1.00
+C139 C 0.49880 0.51600 0.60410 0.03812 Uiso 1.00
+C140 C 0.35040 0.27700 0.70050 0.03812 Uiso 1.00
+C141 C 0.49870 0.73800 0.55810 0.03812 Uiso 1.00
+N142 N 0.66090 0.04400 0.68040 0.03812 Uiso 1.00
+O143 O 0.35220 0.04000 0.73250 0.03812 Uiso 1.00
+O144 O 0.29200 0.44700 0.69010 0.03812 Uiso 1.00
+O145 O 0.42140 0.86500 0.54110 0.03812 Uiso 1.00
+O146 O 0.56460 0.77600 0.53710 0.03812 Uiso 1.00
+H147 H 0.63590 0.41900 0.60590 0.03812 Uiso 1.00
+H148 H 0.49580 0.98200 0.71510 0.03812 Uiso 1.00
+H149 H 0.37140 0.64800 0.61700 0.03812 Uiso 1.00
+H150 H 0.64650 0.80800 0.69730 0.03812 Uiso 1.00
+H151 H 0.70140 0.02900 0.65320 0.03812 Uiso 1.00
+H152 H 0.43390 0.98800 0.51120 0.03812 Uiso 1.00
+Co153 Co 0.25000 0.75000 0.25000 0.00798 Uiso 1.00
+Co154 Co 0.25000 0.75000 0.75000 0.00798 Uiso 1.00
+Co155 Co 0.75000 0.25000 0.75000 0.00798 Uiso 1.00
+Co156 Co 0.75000 0.25000 0.25000 0.00798 Uiso 1.00
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_2
+_ccdc_geom_bond_type
+C1 C2 1.454 . S
+C1 C6 1.349 1_545 D
+C1 H16 1.102 1_545 S
+C2 C3 1.352 . D
+C2 C7 1.502 . S
+C3 C4 1.467 . S
+C3 H15 1.013 . S
+C4 C5 1.374 . D
+C4 N9 1.372 . S
+C5 C6 1.451 1_545 S
+C5 H14 0.979 . S
+C6 C1 1.349 1_565 D
+C6 C5 1.451 1_565 S
+C6 C8 1.541 . S
+C7 O10 1.343 . D
+C7 O11 1.190 . S
+C8 O12 1.349 . S
+C8 O13 1.220 . D
+N9 H17 1.180 . S
+N9 H18 0.992 . S
+O10 Co153 2.141 . S
+O11 Co153 2.205 1_545 S
+O12 H19 0.981 . S
+H16 C1 1.102 1_565 S
+C20 C21 1.454 . S
+C20 C25 1.349 . D
+C20 H35 1.102 . S
+C21 C22 1.352 . D
+C21 C26 1.502 . S
+C22 C23 1.467 . S
+C22 H34 1.013 1_565 S
+C23 C24 1.374 . D
+C23 N28 1.372 . S
+C24 C25 1.451 . S
+C24 H33 0.979 . S
+C25 C27 1.541 . S
+C26 O29 1.343 . D
+C26 O30 1.190 . S
+C27 O31 1.349 . S
+C27 O32 1.220 . D
+N28 H36 1.180 1_565 S
+N28 H37 0.992 . S
+O29 Co155 2.141 1_565 S
+O30 Co155 2.205 . S
+O31 H38 0.981 . S
+H34 C22 1.013 1_545 S
+H36 N28 1.180 1_545 S
+C39 C40 1.454 . S
+C39 C44 1.349 1_545 D
+C39 H54 1.102 1_545 S
+C40 C41 1.352 . D
+C40 C45 1.502 . S
+C41 C42 1.467 . S
+C41 H53 1.013 . S
+C42 C43 1.374 . D
+C42 N47 1.372 . S
+C43 C44 1.451 1_545 S
+C43 H52 0.979 . S
+C44 C39 1.349 1_565 D
+C44 C43 1.451 1_565 S
+C44 C46 1.541 . S
+C45 O48 1.343 . D
+C45 O49 1.190 . S
+C46 O50 1.349 . S
+C46 O51 1.220 . D
+N47 H55 1.180 . S
+N47 H56 0.992 . S
+O48 Co154 2.141 . S
+O49 Co154 2.205 1_545 S
+O50 H57 0.981 . S
+H54 C39 1.102 1_565 S
+C58 C59 1.454 . S
+C58 C63 1.349 . D
+C58 H73 1.102 . S
+C59 C60 1.352 . D
+C59 C64 1.502 . S
+C60 C61 1.467 . S
+C60 H72 1.013 1_565 S
+C61 C62 1.374 . D
+C61 N66 1.372 . S
+C62 C63 1.451 . S
+C62 H71 0.979 . S
+C63 C65 1.541 . S
+C64 O67 1.343 . D
+C64 O68 1.190 . S
+C65 O69 1.349 . S
+C65 O70 1.220 . D
+N66 H74 1.180 1_565 S
+N66 H75 0.992 . S
+O67 Co156 2.141 1_565 S
+O68 Co156 2.205 . S
+O69 H76 0.981 . S
+H72 C60 1.013 1_545 S
+H74 N66 1.180 1_545 S
+C77 C78 1.454 . S
+C77 C82 1.349 1_565 D
+C77 H92 1.102 1_565 S
+C78 C79 1.352 . D
+C78 C83 1.502 . S
+C79 C80 1.467 . S
+C79 H91 1.013 . S
+C80 C81 1.374 . D
+C80 N85 1.372 . S
+C81 C82 1.451 1_565 S
+C81 H90 0.979 . S
+C82 C77 1.349 1_545 D
+C82 C81 1.451 1_545 S
+C82 C84 1.541 . S
+C83 O86 1.343 . D
+C83 O87 1.190 . S
+C84 O88 1.349 . S
+C84 O89 1.220 . D
+N85 H93 1.180 . S
+N85 H94 0.992 . S
+O86 Co155 2.141 . S
+O87 Co155 2.205 1_565 S
+O88 H95 0.981 . S
+H92 C77 1.102 1_545 S
+C96 C97 1.454 . S
+C96 C101 1.349 . D
+C96 H111 1.102 . S
+C97 C98 1.352 . D
+C97 C102 1.502 . S
+C98 C99 1.467 . S
+C98 H110 1.013 1_545 S
+C99 C100 1.374 . D
+C99 N104 1.372 . S
+C100 C101 1.451 . S
+C100 H109 0.979 . S
+C101 C103 1.541 . S
+C102 O105 1.343 . D
+C102 O106 1.190 . S
+C103 O107 1.349 . S
+C103 O108 1.220 . D
+N104 H112 1.180 1_545 S
+N104 H113 0.992 . S
+O105 Co153 2.141 1_545 S
+O106 Co153 2.205 . S
+O107 H114 0.981 . S
+H110 C98 1.013 1_565 S
+H112 N104 1.180 1_565 S
+C115 C116 1.454 . S
+C115 C120 1.349 1_565 D
+C115 H130 1.102 1_565 S
+C116 C117 1.352 . D
+C116 C121 1.502 . S
+C117 C118 1.467 . S
+C117 H129 1.013 . S
+C118 C119 1.374 . D
+C118 N123 1.372 . S
+C119 C120 1.451 1_565 S
+C119 H128 0.979 . S
+C120 C115 1.349 1_545 D
+C120 C119 1.451 1_545 S
+C120 C122 1.541 . S
+C121 O124 1.343 . D
+C121 O125 1.190 . S
+C122 O126 1.349 . S
+C122 O127 1.220 . D
+N123 H131 1.180 . S
+N123 H132 0.992 . S
+O124 Co156 2.141 . S
+O125 Co156 2.205 1_565 S
+O126 H133 0.981 . S
+H130 C115 1.102 1_545 S
+C134 C135 1.454 . S
+C134 C139 1.349 . D
+C134 H149 1.102 . S
+C135 C136 1.352 . D
+C135 C140 1.502 . S
+C136 C137 1.467 . S
+C136 H148 1.013 1_545 S
+C137 C138 1.374 . D
+C137 N142 1.372 . S
+C138 C139 1.451 . S
+C138 H147 0.979 . S
+C139 C141 1.541 . S
+C140 O143 1.343 . D
+C140 O144 1.190 . S
+C141 O145 1.349 . S
+C141 O146 1.220 . D
+N142 H150 1.180 1_545 S
+N142 H151 0.992 . S
+O143 Co154 2.141 1_545 S
+O144 Co154 2.205 . S
+O145 H152 0.981 . S
+H148 C136 1.013 1_565 S
+H150 N142 1.180 1_565 S
+Co153 O105 2.141 1_565 S
+Co153 O11 2.205 1_565 S
+Co154 O143 2.141 1_565 S
+Co154 O49 2.205 1_565 S
+Co155 O29 2.141 1_545 S
+Co155 O87 2.205 1_545 S
+Co156 O67 2.141 1_545 S
+Co156 O125 2.205 1_545 S
diff --git a/benchmarks/mof/structures/flue_gas/UTSA-16.cif b/benchmarks/mof/structures/flue_gas/UTSA-16.cif
new file mode 100644
index 0000000000000000000000000000000000000000..ac9cc8252fc218562cbdf6a25e8e43ace228609b
--- /dev/null
+++ b/benchmarks/mof/structures/flue_gas/UTSA-16.cif
@@ -0,0 +1,176 @@
+data_RAZXIA_clean
+_audit_creation_date 2014-07-02
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 12.9064
+_cell_length_b 12.9064
+_cell_length_c 17.6614
+_cell_angle_alpha 111.4310
+_cell_angle_beta 111.4310
+_cell_angle_gamma 90.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
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+K2 K 0.12500 0.03959 0.75000 0.01267 Uiso 1.00
+K3 K 0.28959 0.87500 0.25000 0.01267 Uiso 1.00
+K4 K 0.96041 0.37500 0.25000 0.01267 Uiso 1.00
+Co1 Co 0.85501 0.91019 0.91934 0.01267 Uiso 1.00
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+H29 H 0.12090 0.46910 0.58420 0.01267 Uiso 1.00
+H30 H 0.05820 0.52850 0.52100 0.01267 Uiso 1.00
+H31 H 0.11410 0.68680 0.66260 0.01267 Uiso 1.00
+H32 H 0.19920 0.63020 0.71860 0.01267 Uiso 1.00
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+C2 C 0.75451 0.87938 0.04648 0.01267 Uiso 1.00
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diff --git a/benchmarks/mof/structures/flue_gas/ZnH-MFU-4l.cif b/benchmarks/mof/structures/flue_gas/ZnH-MFU-4l.cif
new file mode 100644
index 0000000000000000000000000000000000000000..f3d07d6a5be274d04bc875d06f5e38d0d481dbd8
--- /dev/null
+++ b/benchmarks/mof/structures/flue_gas/ZnH-MFU-4l.cif
@@ -0,0 +1,1550 @@
+data_ZnH-MFU-4l
+_audit_creation_date 2025-01-22
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 31.0196
+_cell_length_b 31.0196
+_cell_length_c 31.0196
+_cell_angle_alpha 90.0000
+_cell_angle_beta 90.0000
+_cell_angle_gamma 90.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+Zn1 Zn 0.31463 0.31463 0.18537 0.05500 Uiso 1.00
+O2 H 0.34403 0.34404 0.15596 0.05760 Uiso 1.00
+Zn3 Zn 0.68537 0.68537 0.18537 0.05500 Uiso 1.00
+O4 H 0.65597 0.65596 0.15596 0.05760 Uiso 1.00
+Zn5 Zn 0.18537 0.18537 0.18537 0.05500 Uiso 1.00
+O6 H 0.15597 0.15596 0.15596 0.05760 Uiso 1.00
+Zn7 Zn 0.68537 0.31463 0.81463 0.05500 Uiso 1.00
+O8 H 0.65597 0.34404 0.84404 0.05760 Uiso 1.00
+Zn9 Zn 0.18537 0.31463 0.31463 0.05500 Uiso 1.00
+O10 H 0.15597 0.34404 0.34404 0.05760 Uiso 1.00
+Zn11 Zn 0.31462 0.68538 0.81462 0.05500 Uiso 1.00
+O12 H 0.34404 0.65596 0.84404 0.05760 Uiso 1.00
+Zn13 Zn 0.31463 0.18537 0.31463 0.05500 Uiso 1.00
+O14 H 0.34403 0.15596 0.34404 0.05760 Uiso 1.00
+Zn15 Zn 0.18538 0.81462 0.81462 0.05500 Uiso 1.00
+O16 H 0.15597 0.84404 0.84404 0.05760 Uiso 1.00
+Zn17 Zn 0.18538 0.68538 0.68538 0.05500 Uiso 1.00
+O18 H 0.15597 0.65596 0.65596 0.05760 Uiso 1.00
+Zn19 Zn 0.81463 0.68537 0.31463 0.05500 Uiso 1.00
+O20 H 0.84403 0.65596 0.34404 0.05760 Uiso 1.00
+Zn21 Zn 0.81463 0.18537 0.81463 0.05500 Uiso 1.00
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+Zn23 Zn 0.81463 0.31463 0.68537 0.05500 Uiso 1.00
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+Zn25 Zn 0.81463 0.81463 0.18537 0.05500 Uiso 1.00
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+Zn27 Zn 0.68537 0.18537 0.68537 0.05500 Uiso 1.00
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+Zn29 Zn 0.31462 0.81462 0.68538 0.05500 Uiso 1.00
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+Zn31 Zn 0.68537 0.81463 0.31463 0.05500 Uiso 1.00
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+Zn33 Zn 0.31463 0.31463 0.81463 0.05500 Uiso 1.00
+O34 H 0.34403 0.34404 0.84404 0.05760 Uiso 1.00
+Zn35 Zn 0.68537 0.68537 0.81463 0.05500 Uiso 1.00
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+Zn37 Zn 0.68537 0.18537 0.31463 0.05500 Uiso 1.00
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+Zn39 Zn 0.18538 0.68538 0.31462 0.05500 Uiso 1.00
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+Zn47 Zn 0.31463 0.18537 0.68537 0.05500 Uiso 1.00
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+Zn49 Zn 0.18537 0.18537 0.81463 0.05500 Uiso 1.00
+O50 H 0.15597 0.15596 0.84404 0.05760 Uiso 1.00
+Zn51 Zn 0.68537 0.81463 0.68537 0.05500 Uiso 1.00
+O52 H 0.65597 0.84404 0.65596 0.05760 Uiso 1.00
+Zn53 Zn 0.18537 0.31463 0.68537 0.05500 Uiso 1.00
+O54 H 0.15597 0.34404 0.65596 0.05760 Uiso 1.00
+Zn55 Zn 0.31462 0.81462 0.31462 0.05500 Uiso 1.00
+O56 H 0.34404 0.84404 0.34404 0.05760 Uiso 1.00
+Zn57 Zn 0.81463 0.31463 0.31463 0.05500 Uiso 1.00
+O58 H 0.84403 0.34404 0.34404 0.05760 Uiso 1.00
+Zn59 Zn 0.81463 0.81463 0.81463 0.05500 Uiso 1.00
+O60 H 0.84403 0.84404 0.84404 0.05760 Uiso 1.00
+Zn61 Zn 0.81463 0.68537 0.68537 0.05500 Uiso 1.00
+O62 H 0.84403 0.65596 0.65596 0.05760 Uiso 1.00
+Zn63 Zn 0.81463 0.18537 0.18537 0.05500 Uiso 1.00
+O64 H 0.84403 0.15596 0.15596 0.05760 Uiso 1.00
+Zn65 Zn 0.25000 0.25000 0.25000 0.05500 Uiso 1.00
+Zn66 Zn 0.75000 0.75000 0.25000 0.05500 Uiso 1.00
+Zn67 Zn 0.75000 0.25000 0.75000 0.05500 Uiso 1.00
+Zn68 Zn 0.25000 0.75000 0.75000 0.05500 Uiso 1.00
+Zn69 Zn 0.25000 0.25000 0.75000 0.05500 Uiso 1.00
+Zn70 Zn 0.75000 0.75000 0.75000 0.05500 Uiso 1.00
+Zn71 Zn 0.75000 0.25000 0.25000 0.05500 Uiso 1.00
+Zn72 Zn 0.25000 0.75000 0.25000 0.05500 Uiso 1.00
+N73 N 0.34050 0.27501 0.22499 0.05600 Uiso 1.00
+C74 C 0.38230 0.26679 0.23321 0.06100 Uiso 1.00
+C75 C 0.42100 0.28292 0.21708 0.06100 Uiso 1.00
+C76 C 0.46090 0.26659 0.23341 0.06100 Uiso 1.00
+H77 H 0.42094 0.30413 0.19587 0.03800 Uiso 1.00
+N78 N 0.84050 0.27501 0.72499 0.05600 Uiso 1.00
+C79 C 0.88230 0.26679 0.73321 0.06100 Uiso 1.00
+C80 C 0.92100 0.28292 0.71708 0.06100 Uiso 1.00
+C81 C 0.96090 0.26659 0.73341 0.06100 Uiso 1.00
+H82 H 0.92094 0.30413 0.69587 0.03800 Uiso 1.00
+N83 N 0.65950 0.72499 0.22499 0.05600 Uiso 1.00
+C84 C 0.61770 0.73321 0.23321 0.06100 Uiso 1.00
+C85 C 0.57900 0.71708 0.21708 0.06100 Uiso 1.00
+C86 C 0.53910 0.73341 0.23341 0.06100 Uiso 1.00
+H87 H 0.57906 0.69587 0.19587 0.03800 Uiso 1.00
+N88 N 0.15950 0.72499 0.72499 0.05600 Uiso 1.00
+C89 C 0.11770 0.73321 0.73321 0.06100 Uiso 1.00
+C90 C 0.07900 0.71708 0.71708 0.06100 Uiso 1.00
+C91 C 0.03910 0.73341 0.73341 0.06100 Uiso 1.00
+H92 H 0.07906 0.69587 0.69587 0.03800 Uiso 1.00
+N93 N 0.15950 0.22499 0.22499 0.05600 Uiso 1.00
+C94 C 0.11770 0.23321 0.23321 0.06100 Uiso 1.00
+C95 C 0.07900 0.21708 0.21708 0.06100 Uiso 1.00
+C96 C 0.03910 0.23341 0.23341 0.06100 Uiso 1.00
+H97 H 0.07906 0.19587 0.19587 0.03800 Uiso 1.00
+N98 N 0.65950 0.27501 0.77501 0.05600 Uiso 1.00
+C99 C 0.61770 0.26679 0.76679 0.06100 Uiso 1.00
+C100 C 0.57900 0.28292 0.78292 0.06100 Uiso 1.00
+C101 C 0.53910 0.26659 0.76659 0.06100 Uiso 1.00
+H102 H 0.57906 0.30413 0.80413 0.03800 Uiso 1.00
+N103 N 0.15950 0.27501 0.27501 0.05600 Uiso 1.00
+C104 C 0.11770 0.26679 0.26679 0.06100 Uiso 1.00
+C105 C 0.07900 0.28292 0.28292 0.06100 Uiso 1.00
+C106 C 0.03910 0.26659 0.26659 0.06100 Uiso 1.00
+H107 H 0.07906 0.30413 0.30413 0.03800 Uiso 1.00
+N108 N 0.34050 0.72499 0.77501 0.05600 Uiso 1.00
+C109 C 0.38230 0.73321 0.76679 0.06100 Uiso 1.00
+C110 C 0.42100 0.71708 0.78292 0.06100 Uiso 1.00
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+N555 N 0.68430 0.75000 0.25000 0.05600 Uiso 1.00
+N556 N 0.18430 0.75000 0.75000 0.05600 Uiso 1.00
+N557 N 0.18430 0.25000 0.25000 0.05600 Uiso 1.00
+N558 N 0.68430 0.25000 0.75000 0.05600 Uiso 1.00
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+N560 N 0.81570 0.75000 0.25000 0.05600 Uiso 1.00
+N561 N 0.25000 0.31570 0.25000 0.05600 Uiso 1.00
+N562 N 0.25000 0.81570 0.75000 0.05600 Uiso 1.00
+N563 N 0.25000 0.68430 0.75000 0.05600 Uiso 1.00
+N564 N 0.25000 0.18430 0.25000 0.05600 Uiso 1.00
+N565 N 0.75000 0.68430 0.25000 0.05600 Uiso 1.00
+N566 N 0.75000 0.18430 0.75000 0.05600 Uiso 1.00
+N567 N 0.75000 0.31570 0.75000 0.05600 Uiso 1.00
+N568 N 0.75000 0.81570 0.25000 0.05600 Uiso 1.00
+N569 N 0.25000 0.25000 0.31570 0.05600 Uiso 1.00
+N570 N 0.25000 0.75000 0.81570 0.05600 Uiso 1.00
+N571 N 0.75000 0.25000 0.68430 0.05600 Uiso 1.00
+N572 N 0.75000 0.75000 0.18430 0.05600 Uiso 1.00
+N573 N 0.25000 0.25000 0.18430 0.05600 Uiso 1.00
+N574 N 0.25000 0.75000 0.68430 0.05600 Uiso 1.00
+N575 N 0.75000 0.75000 0.31570 0.05600 Uiso 1.00
+N576 N 0.75000 0.25000 0.81570 0.05600 Uiso 1.00
+N577 N 0.25000 0.31570 0.75000 0.05600 Uiso 1.00
+N578 N 0.25000 0.81570 0.25000 0.05600 Uiso 1.00
+N579 N 0.75000 0.68430 0.75000 0.05600 Uiso 1.00
+N580 N 0.75000 0.18430 0.25000 0.05600 Uiso 1.00
+N581 N 0.25000 0.68430 0.25000 0.05600 Uiso 1.00
+N582 N 0.25000 0.18430 0.75000 0.05600 Uiso 1.00
+N583 N 0.75000 0.31570 0.25000 0.05600 Uiso 1.00
+N584 N 0.75000 0.81570 0.75000 0.05600 Uiso 1.00
+N585 N 0.31570 0.25000 0.75000 0.05600 Uiso 1.00
+N586 N 0.81570 0.25000 0.25000 0.05600 Uiso 1.00
+N587 N 0.68430 0.25000 0.25000 0.05600 Uiso 1.00
+N588 N 0.18430 0.25000 0.75000 0.05600 Uiso 1.00
+N589 N 0.68430 0.75000 0.75000 0.05600 Uiso 1.00
+N590 N 0.18430 0.75000 0.25000 0.05600 Uiso 1.00
+N591 N 0.31570 0.75000 0.25000 0.05600 Uiso 1.00
+N592 N 0.81570 0.75000 0.75000 0.05600 Uiso 1.00
+N593 N 0.25000 0.25000 0.68430 0.05600 Uiso 1.00
+N594 N 0.25000 0.75000 0.18430 0.05600 Uiso 1.00
+N595 N 0.25000 0.75000 0.31570 0.05600 Uiso 1.00
+N596 N 0.25000 0.25000 0.81570 0.05600 Uiso 1.00
+N597 N 0.75000 0.25000 0.31570 0.05600 Uiso 1.00
+N598 N 0.75000 0.75000 0.81570 0.05600 Uiso 1.00
+N599 N 0.75000 0.75000 0.68430 0.05600 Uiso 1.00
+N600 N 0.75000 0.25000 0.18430 0.05600 Uiso 1.00
+O601 O 0.50000 0.28282 0.21718 0.06100 Uiso 1.00
+O602 O 0.00000 0.28282 0.71718 0.06100 Uiso 1.00
+O603 O 0.50000 0.71718 0.21718 0.06100 Uiso 1.00
+O604 O 0.00000 0.71718 0.71718 0.06100 Uiso 1.00
+O605 O 0.50000 0.28282 0.78282 0.06100 Uiso 1.00
+O606 O 0.00000 0.28282 0.28282 0.06100 Uiso 1.00
+O607 O 0.50000 0.71718 0.78282 0.06100 Uiso 1.00
+O608 O 0.00000 0.71718 0.28282 0.06100 Uiso 1.00
+O609 O 0.21718 0.50000 0.28282 0.06100 Uiso 1.00
+O610 O 0.21718 0.00000 0.78282 0.06100 Uiso 1.00
+O611 O 0.21718 0.50000 0.71718 0.06100 Uiso 1.00
+O612 O 0.21718 0.00000 0.21718 0.06100 Uiso 1.00
+O613 O 0.78282 0.50000 0.28282 0.06100 Uiso 1.00
+O614 O 0.78282 0.00000 0.78282 0.06100 Uiso 1.00
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+O632 O 0.71718 0.00000 0.71718 0.06100 Uiso 1.00
+O633 O 0.50000 0.21718 0.71718 0.06100 Uiso 1.00
+O634 O 0.00000 0.21718 0.21718 0.06100 Uiso 1.00
+O635 O 0.50000 0.21718 0.28282 0.06100 Uiso 1.00
+O636 O 0.00000 0.21718 0.78282 0.06100 Uiso 1.00
+O637 O 0.50000 0.78282 0.71718 0.06100 Uiso 1.00
+O638 O 0.00000 0.78282 0.21718 0.06100 Uiso 1.00
+O639 O 0.50000 0.78282 0.28282 0.06100 Uiso 1.00
+O640 O 0.00000 0.78282 0.78282 0.06100 Uiso 1.00
+O641 O 0.21718 0.28282 0.50000 0.06100 Uiso 1.00
+O642 O 0.21718 0.78282 0.00000 0.06100 Uiso 1.00
+O643 O 0.21718 0.71718 0.50000 0.06100 Uiso 1.00
+O644 O 0.21718 0.21718 0.00000 0.06100 Uiso 1.00
+O645 O 0.78282 0.28282 0.50000 0.06100 Uiso 1.00
+O646 O 0.78282 0.78282 0.00000 0.06100 Uiso 1.00
+O647 O 0.78282 0.71718 0.50000 0.06100 Uiso 1.00
+O648 O 0.78282 0.21718 0.00000 0.06100 Uiso 1.00
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_2
+_ccdc_geom_bond_type
+Zn1 O2 1.580 . S
+Zn1 N73 1.914 . S
+Zn1 N168 1.914 . S
+Zn1 N203 1.914 . S
+Zn3 O4 1.580 . S
+Zn3 N83 1.914 . S
+Zn3 N463 1.914 . S
+Zn3 N188 1.914 . S
+Zn5 O6 1.580 . S
+Zn5 N138 1.914 . S
+Zn5 N193 1.914 . S
+Zn5 N93 1.914 . S
+Zn7 O8 1.580 . S
+Zn7 N98 1.914 . S
+Zn7 N478 1.914 . S
+Zn7 N213 1.914 . S
+Zn9 O10 1.580 . S
+Zn9 N123 1.914 . S
+Zn9 N218 1.914 . S
+Zn9 N103 1.914 . S
+Zn11 O12 1.580 . S
+Zn11 N108 1.914 . S
+Zn11 N488 1.914 . S
+Zn11 N178 1.914 . S
+Zn13 O14 1.580 . S
+Zn13 N173 1.914 . S
+Zn13 N113 1.914 . S
+Zn13 N153 1.914 . S
+Zn15 O16 1.580 . S
+Zn15 N128 1.914 . S
+Zn15 N548 1.914 . S
+Zn15 N498 1.914 . S
+Zn17 O18 1.580 . S
+Zn17 N133 1.914 . S
+Zn17 N543 1.914 . S
+Zn17 N88 1.914 . S
+Zn19 O20 1.580 . S
+Zn19 N143 1.914 . S
+Zn19 N523 1.914 . S
+Zn19 N118 1.914 . S
+Zn21 O22 1.580 . S
+Zn21 N148 1.914 . S
+Zn21 N528 1.914 . S
+Zn21 N513 1.914 . S
+Zn23 O24 1.580 . S
+Zn23 N158 1.914 . S
+Zn23 N533 1.914 . S
+Zn23 N78 1.914 . S
+Zn25 O26 1.580 . S
+Zn25 N163 1.914 . S
+Zn25 N538 1.914 . S
+Zn25 N508 1.914 . S
+Zn27 O28 1.580 . S
+Zn27 N183 1.914 . S
+Zn27 N518 1.914 . S
+Zn27 N468 1.914 . S
+Zn29 O30 1.580 . S
+Zn29 N198 1.914 . S
+Zn29 N503 1.914 . S
+Zn29 N473 1.914 . S
+Zn31 O32 1.580 . S
+Zn31 N208 1.914 . S
+Zn31 N493 1.914 . S
+Zn31 N483 1.914 . S
+Zn33 O34 1.580 . S
+Zn33 N223 1.914 . S
+Zn33 N383 1.914 . S
+Zn33 N353 1.914 . S
+Zn35 O36 1.580 . S
+Zn35 N233 1.914 . S
+Zn35 N373 1.914 . S
+Zn35 N453 1.914 . S
+Zn37 O38 1.580 . S
+Zn37 N283 1.914 . S
+Zn37 N448 1.914 . S
+Zn37 N238 1.914 . S
+Zn39 O40 1.580 . S
+Zn39 N333 1.914 . S
+Zn39 N243 1.914 . S
+Zn39 N378 1.914 . S
+Zn41 O42 1.580 . S
+Zn41 N248 1.914 . S
+Zn41 N388 1.914 . S
+Zn41 N458 1.914 . S
+Zn43 O44 1.580 . S
+Zn43 N258 1.914 . S
+Zn43 N298 1.914 . S
+Zn43 N438 1.914 . S
+Zn45 O46 1.580 . S
+Zn45 N328 1.914 . S
+Zn45 N268 1.914 . S
+Zn45 N303 1.914 . S
+Zn47 O48 1.580 . S
+Zn47 N273 1.914 . S
+Zn47 N368 1.914 . S
+Zn47 N253 1.914 . S
+Zn49 O50 1.580 . S
+Zn49 N338 1.914 . S
+Zn49 N428 1.914 . S
+Zn49 N288 1.914 . S
+Zn51 O52 1.580 . S
+Zn51 N293 1.914 . S
+Zn51 N433 1.914 . S
+Zn51 N263 1.914 . S
+Zn53 O54 1.580 . S
+Zn53 N323 1.914 . S
+Zn53 N423 1.914 . S
+Zn53 N398 1.914 . S
+Zn55 O56 1.580 . S
+Zn55 N308 1.914 . S
+Zn55 N443 1.914 . S
+Zn55 N228 1.914 . S
+Zn57 O58 1.580 . S
+Zn57 N343 1.914 . S
+Zn57 N413 1.914 . S
+Zn57 N318 1.914 . S
+Zn59 O60 1.580 . S
+Zn59 N348 1.914 . S
+Zn59 N418 1.914 . S
+Zn59 N313 1.914 . S
+Zn61 O62 1.580 . S
+Zn61 N358 1.914 . S
+Zn61 N403 1.914 . S
+Zn61 N393 1.914 . S
+Zn63 O64 1.580 . S
+Zn63 N363 1.914 . S
+Zn63 N408 1.914 . S
+Zn63 N278 1.914 . S
+Zn65 N553 2.038 . S
+Zn65 N561 2.038 . S
+Zn65 N569 2.038 . S
+Zn65 N564 2.038 . S
+Zn65 N573 2.038 . S
+Zn65 N557 2.038 . S
+Zn66 N555 2.038 . S
+Zn66 N565 2.038 . S
+Zn66 N575 2.038 . S
+Zn66 N568 2.038 . S
+Zn66 N572 2.038 . S
+Zn66 N560 2.038 . S
+Zn67 N558 2.038 . S
+Zn67 N567 2.038 . S
+Zn67 N571 2.038 . S
+Zn67 N566 2.038 . S
+Zn67 N576 2.038 . S
+Zn67 N554 2.038 . S
+Zn68 N559 2.038 . S
+Zn68 N563 2.038 . S
+Zn68 N574 2.038 . S
+Zn68 N562 2.038 . S
+Zn68 N570 2.038 . S
+Zn68 N556 2.038 . S
+Zn69 N577 2.038 . S
+Zn69 N585 2.038 . S
+Zn69 N593 2.038 . S
+Zn69 N588 2.038 . S
+Zn69 N596 2.038 . S
+Zn69 N582 2.038 . S
+Zn70 N579 2.038 . S
+Zn70 N589 2.038 . S
+Zn70 N599 2.038 . S
+Zn70 N592 2.038 . S
+Zn70 N598 2.038 . S
+Zn70 N584 2.038 . S
+Zn71 N583 2.038 . S
+Zn71 N587 2.038 . S
+Zn71 N597 2.038 . S
+Zn71 N586 2.038 . S
+Zn71 N600 2.038 . S
+Zn71 N580 2.038 . S
+Zn72 N581 2.038 . S
+Zn72 N591 2.038 . S
+Zn72 N595 2.038 . S
+Zn72 N590 2.038 . S
+Zn72 N594 2.038 . S
+Zn72 N578 2.038 . S
+N73 C74 1.346 . D
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+C516 O636 1.406 1_655 S
+N518 C519 1.346 . D
+N518 N558 1.340 . S
+C519 C520 1.393 . S
+C520 C521 1.430 . D
+C520 H522 0.930 . S
+C521 O633 1.406 . S
+N523 C524 1.346 . D
+N523 N575 1.340 . S
+C524 C525 1.393 . S
+C525 C526 1.430 . D
+C525 H527 0.930 . S
+C526 O647 1.406 . S
+N528 C529 1.346 . D
+N528 N576 1.340 . S
+C529 C530 1.393 . S
+C530 C531 1.430 . D
+C530 H532 0.930 . S
+C531 O648 1.406 1_556 S
+N533 C534 1.346 . D
+N533 N571 1.340 . S
+C534 C535 1.393 . S
+C535 C536 1.430 . D
+C535 H537 0.930 . S
+C536 O645 1.406 . S
+N538 C539 1.346 . D
+N538 N572 1.340 . S
+C539 C540 1.393 . S
+C540 C541 1.430 . D
+C540 H542 0.930 . S
+C541 O646 1.406 . S
+N543 C544 1.346 . D
+N543 N574 1.340 . S
+C544 C545 1.393 . S
+C545 C546 1.430 . D
+C545 H547 0.930 . S
+C546 O643 1.406 . S
+N548 C549 1.346 . D
+N548 N570 1.340 . S
+C549 C550 1.393 . S
+C550 C551 1.430 . D
+C550 H552 0.930 . S
+C551 O642 1.406 1_556 S
+O602 C81 1.406 1_455 S
+O604 C396 1.406 1_455 S
+O606 C321 1.406 1_455 S
+O608 C121 1.406 1_455 S
+O610 C131 1.406 1_545 S
+O612 C271 1.406 1_545 S
+O614 C421 1.406 1_545 S
+O616 C166 1.406 1_545 S
+O618 C181 1.406 1_554 S
+O620 C456 1.406 1_554 S
+O622 C356 1.406 1_554 S
+O624 C216 1.406 1_554 S
+O626 C231 1.406 1_545 S
+O628 C486 1.406 1_545 S
+O630 C476 1.406 1_545 S
+O632 C266 1.406 1_545 S
+O634 C281 1.406 1_455 S
+O636 C516 1.406 1_455 S
+O638 C511 1.406 1_455 S
+O640 C316 1.406 1_455 S
+O642 C551 1.406 1_554 S
+O644 C341 1.406 1_554 S
+O646 C351 1.406 1_554 S
+O648 C531 1.406 1_554 S
diff --git a/benchmarks/mof/structures/general/Fe-MOF-74.cif b/benchmarks/mof/structures/general/Fe-MOF-74.cif
new file mode 100644
index 0000000000000000000000000000000000000000..07f3ba17d17e35152a3e696b107a609049963bf7
--- /dev/null
+++ b/benchmarks/mof/structures/general/Fe-MOF-74.cif
@@ -0,0 +1,186 @@
+data_image0
+_cell_length_a 26.179
+_cell_length_b 26.179
+_cell_length_c 6.652
+_cell_angle_alpha 90
+_cell_angle_beta 90
+_cell_angle_gamma 120
+
+_symmetry_space_group_name_H-M "P 1"
+_symmetry_int_tables_number 1
+
+loop_
+ _symmetry_equiv_pos_as_xyz
+ 'x, y, z'
+
+loop_
+ _atom_site_label
+ _atom_site_occupancy
+ _atom_site_fract_x
+ _atom_site_fract_y
+ _atom_site_fract_z
+ _atom_site_thermal_displace_type
+ _atom_site_B_iso_or_equiv
+ _atom_site_type_symbol
+ Fe1 1.0000 0.38790 0.35068 0.15211 Biso 1.000 Fe
+ C1 1.0000 0.32290 0.20386 0.28779 Biso 1.000 C
+ C2 1.0000 0.34291 0.22040 0.09164 Biso 1.000 C
+ C3 1.0000 0.35335 0.18320 0.97052 Biso 1.000 C
+ H1 1.0000 0.36889 0.19605 0.81817 Biso 1.000 H
+ C4 1.0000 0.31168 0.24386 0.41805 Biso 1.000 C
+ O1 1.0000 0.32032 0.29230 0.35040 Biso 1.000 O
+ O2 1.0000 0.29364 0.22895 0.59490 Biso 1.000 O
+ O3 1.0000 0.35220 0.27243 0.01900 Biso 1.000 O
+ Fe2 1.0000 0.05457 0.68401 0.48544 Biso 1.000 Fe
+ C5 1.0000 0.98957 0.53719 0.62112 Biso 1.000 C
+ C6 1.0000 0.00958 0.55373 0.42497 Biso 1.000 C
+ C7 1.0000 0.02002 0.51653 0.30385 Biso 1.000 C
+ H2 1.0000 0.03556 0.52938 0.15150 Biso 1.000 H
+ C8 1.0000 0.97835 0.57719 0.75138 Biso 1.000 C
+ O4 1.0000 0.98699 0.62563 0.68373 Biso 1.000 O
+ O5 1.0000 0.96031 0.56228 0.92823 Biso 1.000 O
+ O6 1.0000 0.01887 0.60576 0.35233 Biso 1.000 O
+ Fe3 1.0000 0.72123 0.01735 0.81878 Biso 1.000 Fe
+ C9 1.0000 0.65623 0.87053 0.95446 Biso 1.000 C
+ C10 1.0000 0.67624 0.88707 0.75831 Biso 1.000 C
+ C11 1.0000 0.68668 0.84987 0.63719 Biso 1.000 C
+ H3 1.0000 0.70222 0.86272 0.48484 Biso 1.000 H
+ C12 1.0000 0.64501 0.91053 0.08472 Biso 1.000 C
+ O7 1.0000 0.65365 0.95897 0.01707 Biso 1.000 O
+ O8 1.0000 0.62697 0.89562 0.26157 Biso 1.000 O
+ O9 1.0000 0.68553 0.93910 0.68567 Biso 1.000 O
+ Fe4 1.0000 0.64932 0.03722 0.15211 Biso 1.000 Fe
+ C13 1.0000 0.79614 0.11904 0.28779 Biso 1.000 C
+ C14 1.0000 0.77960 0.12251 0.09164 Biso 1.000 C
+ C15 1.0000 0.81680 0.17015 0.97052 Biso 1.000 C
+ H4 1.0000 0.80395 0.17284 0.81817 Biso 1.000 H
+ C16 1.0000 0.75614 0.06782 0.41805 Biso 1.000 C
+ O10 1.0000 0.70770 0.02802 0.35040 Biso 1.000 O
+ O11 1.0000 0.77105 0.06469 0.59490 Biso 1.000 O
+ O12 1.0000 0.72757 0.07977 0.01900 Biso 1.000 O
+ Fe5 1.0000 0.31599 0.37055 0.48544 Biso 1.000 Fe
+ C17 1.0000 0.46281 0.45237 0.62112 Biso 1.000 C
+ C18 1.0000 0.44627 0.45584 0.42497 Biso 1.000 C
+ C19 1.0000 0.48347 0.50348 0.30385 Biso 1.000 C
+ H5 1.0000 0.47062 0.50617 0.15150 Biso 1.000 H
+ C20 1.0000 0.42281 0.40115 0.75138 Biso 1.000 C
+ O13 1.0000 0.37437 0.36135 0.68373 Biso 1.000 O
+ O14 1.0000 0.43772 0.39802 0.92823 Biso 1.000 O
+ O15 1.0000 0.39424 0.41310 0.35233 Biso 1.000 O
+ Fe6 1.0000 0.98265 0.70389 0.81878 Biso 1.000 Fe
+ C21 1.0000 0.12947 0.78571 0.95446 Biso 1.000 C
+ C22 1.0000 0.11293 0.78918 0.75831 Biso 1.000 C
+ C23 1.0000 0.15013 0.83682 0.63719 Biso 1.000 C
+ H6 1.0000 0.13728 0.83951 0.48484 Biso 1.000 H
+ C24 1.0000 0.08947 0.73449 0.08472 Biso 1.000 C
+ O16 1.0000 0.04103 0.69469 0.01707 Biso 1.000 O
+ O17 1.0000 0.10438 0.73136 0.26157 Biso 1.000 O
+ O18 1.0000 0.06090 0.74644 0.68567 Biso 1.000 O
+ Fe7 1.0000 0.96278 0.61210 0.15211 Biso 1.000 Fe
+ C25 1.0000 0.88096 0.67710 0.28779 Biso 1.000 C
+ C26 1.0000 0.87749 0.65709 0.09164 Biso 1.000 C
+ C27 1.0000 0.82985 0.64665 0.97052 Biso 1.000 C
+ H7 1.0000 0.82716 0.63111 0.81817 Biso 1.000 H
+ C28 1.0000 0.93218 0.68832 0.41805 Biso 1.000 C
+ O19 1.0000 0.97198 0.67968 0.35040 Biso 1.000 O
+ O20 1.0000 0.93531 0.70636 0.59490 Biso 1.000 O
+ O21 1.0000 0.92023 0.64780 0.01900 Biso 1.000 O
+ Fe8 1.0000 0.62945 0.94543 0.48544 Biso 1.000 Fe
+ C29 1.0000 0.54763 0.01043 0.62112 Biso 1.000 C
+ C30 1.0000 0.54416 0.99042 0.42497 Biso 1.000 C
+ C31 1.0000 0.49652 0.97998 0.30385 Biso 1.000 C
+ H8 1.0000 0.49383 0.96444 0.15150 Biso 1.000 H
+ C32 1.0000 0.59885 0.02165 0.75138 Biso 1.000 C
+ O22 1.0000 0.63865 0.01301 0.68373 Biso 1.000 O
+ O23 1.0000 0.60198 0.03969 0.92823 Biso 1.000 O
+ O24 1.0000 0.58690 0.98113 0.35233 Biso 1.000 O
+ Fe9 1.0000 0.29611 0.27877 0.81878 Biso 1.000 Fe
+ C33 1.0000 0.21429 0.34377 0.95446 Biso 1.000 C
+ C34 1.0000 0.21082 0.32376 0.75831 Biso 1.000 C
+ C35 1.0000 0.16318 0.31332 0.63719 Biso 1.000 C
+ H9 1.0000 0.16049 0.29778 0.48484 Biso 1.000 H
+ C36 1.0000 0.26551 0.35499 0.08472 Biso 1.000 C
+ O25 1.0000 0.30531 0.34635 0.01707 Biso 1.000 O
+ O26 1.0000 0.26864 0.37303 0.26157 Biso 1.000 O
+ O27 1.0000 0.25356 0.31447 0.68567 Biso 1.000 O
+ Fe10 1.0000 0.61210 0.64932 0.84789 Biso 1.000 Fe
+ C37 1.0000 0.67710 0.79614 0.71221 Biso 1.000 C
+ C38 1.0000 0.65709 0.77960 0.90836 Biso 1.000 C
+ C39 1.0000 0.64665 0.81680 0.02948 Biso 1.000 C
+ H10 1.0000 0.63111 0.80395 0.18183 Biso 1.000 H
+ C40 1.0000 0.68832 0.75614 0.58195 Biso 1.000 C
+ O28 1.0000 0.67968 0.70770 0.64960 Biso 1.000 O
+ O29 1.0000 0.70636 0.77105 0.40510 Biso 1.000 O
+ O30 1.0000 0.64780 0.72757 0.98100 Biso 1.000 O
+ Fe11 1.0000 0.27877 0.98265 0.18122 Biso 1.000 Fe
+ C41 1.0000 0.34377 0.12947 0.04554 Biso 1.000 C
+ C42 1.0000 0.32376 0.11293 0.24169 Biso 1.000 C
+ C43 1.0000 0.31332 0.15013 0.36281 Biso 1.000 C
+ H11 1.0000 0.29778 0.13728 0.51516 Biso 1.000 H
+ C44 1.0000 0.35499 0.08947 0.91528 Biso 1.000 C
+ O31 1.0000 0.34635 0.04103 0.98293 Biso 1.000 O
+ O32 1.0000 0.37303 0.10438 0.73843 Biso 1.000 O
+ O33 1.0000 0.31447 0.06090 0.31433 Biso 1.000 O
+ Fe12 1.0000 0.94543 0.31599 0.51456 Biso 1.000 Fe
+ C45 1.0000 0.01043 0.46281 0.37888 Biso 1.000 C
+ C46 1.0000 0.99042 0.44627 0.57503 Biso 1.000 C
+ C47 1.0000 0.97998 0.48347 0.69615 Biso 1.000 C
+ H12 1.0000 0.96444 0.47062 0.84850 Biso 1.000 H
+ C48 1.0000 0.02165 0.42281 0.24862 Biso 1.000 C
+ O34 1.0000 0.01301 0.37437 0.31627 Biso 1.000 O
+ O35 1.0000 0.03969 0.43772 0.07177 Biso 1.000 O
+ O36 1.0000 0.98113 0.39424 0.64767 Biso 1.000 O
+ Fe13 1.0000 0.35068 0.96278 0.84789 Biso 1.000 Fe
+ C49 1.0000 0.20386 0.88096 0.71221 Biso 1.000 C
+ C50 1.0000 0.22040 0.87749 0.90836 Biso 1.000 C
+ C51 1.0000 0.18320 0.82985 0.02948 Biso 1.000 C
+ H13 1.0000 0.19605 0.82716 0.18183 Biso 1.000 H
+ C52 1.0000 0.24386 0.93218 0.58195 Biso 1.000 C
+ O37 1.0000 0.29230 0.97198 0.64960 Biso 1.000 O
+ O38 1.0000 0.22895 0.93531 0.40510 Biso 1.000 O
+ O39 1.0000 0.27243 0.92023 0.98100 Biso 1.000 O
+ Fe14 1.0000 0.01735 0.29611 0.18122 Biso 1.000 Fe
+ C53 1.0000 0.87053 0.21429 0.04554 Biso 1.000 C
+ C54 1.0000 0.88707 0.21082 0.24169 Biso 1.000 C
+ C55 1.0000 0.84987 0.16318 0.36281 Biso 1.000 C
+ H14 1.0000 0.86272 0.16049 0.51516 Biso 1.000 H
+ C56 1.0000 0.91053 0.26551 0.91528 Biso 1.000 C
+ O40 1.0000 0.95897 0.30531 0.98293 Biso 1.000 O
+ O41 1.0000 0.89562 0.26864 0.73843 Biso 1.000 O
+ O42 1.0000 0.93910 0.25356 0.31433 Biso 1.000 O
+ Fe15 1.0000 0.68401 0.62945 0.51456 Biso 1.000 Fe
+ C57 1.0000 0.53719 0.54763 0.37888 Biso 1.000 C
+ C58 1.0000 0.55373 0.54416 0.57503 Biso 1.000 C
+ C59 1.0000 0.51653 0.49652 0.69615 Biso 1.000 C
+ H15 1.0000 0.52938 0.49383 0.84850 Biso 1.000 H
+ C60 1.0000 0.57719 0.59885 0.24862 Biso 1.000 C
+ O43 1.0000 0.62563 0.63865 0.31627 Biso 1.000 O
+ O44 1.0000 0.56228 0.60198 0.07177 Biso 1.000 O
+ O45 1.0000 0.60576 0.58690 0.64767 Biso 1.000 O
+ Fe16 1.0000 0.03722 0.38790 0.84789 Biso 1.000 Fe
+ C61 1.0000 0.11904 0.32290 0.71221 Biso 1.000 C
+ C62 1.0000 0.12251 0.34291 0.90836 Biso 1.000 C
+ C63 1.0000 0.17015 0.35335 0.02948 Biso 1.000 C
+ H16 1.0000 0.17284 0.36889 0.18183 Biso 1.000 H
+ C64 1.0000 0.06782 0.31168 0.58195 Biso 1.000 C
+ O46 1.0000 0.02802 0.32032 0.64960 Biso 1.000 O
+ O47 1.0000 0.06469 0.29364 0.40510 Biso 1.000 O
+ O48 1.0000 0.07977 0.35220 0.98100 Biso 1.000 O
+ Fe17 1.0000 0.70389 0.72123 0.18122 Biso 1.000 Fe
+ C65 1.0000 0.78571 0.65623 0.04554 Biso 1.000 C
+ C66 1.0000 0.78918 0.67624 0.24169 Biso 1.000 C
+ C67 1.0000 0.83682 0.68668 0.36281 Biso 1.000 C
+ H17 1.0000 0.83951 0.70222 0.51516 Biso 1.000 H
+ C68 1.0000 0.73449 0.64501 0.91528 Biso 1.000 C
+ O49 1.0000 0.69469 0.65365 0.98293 Biso 1.000 O
+ O50 1.0000 0.73136 0.62697 0.73843 Biso 1.000 O
+ O51 1.0000 0.74644 0.68553 0.31433 Biso 1.000 O
+ Fe18 1.0000 0.37055 0.05457 0.51456 Biso 1.000 Fe
+ C69 1.0000 0.45237 0.98957 0.37888 Biso 1.000 C
+ C70 1.0000 0.45584 0.00958 0.57503 Biso 1.000 C
+ C71 1.0000 0.50348 0.02002 0.69615 Biso 1.000 C
+ H18 1.0000 0.50617 0.03556 0.84850 Biso 1.000 H
+ C72 1.0000 0.40115 0.97835 0.24862 Biso 1.000 C
+ O52 1.0000 0.36135 0.98699 0.31627 Biso 1.000 O
+ O53 1.0000 0.39802 0.96031 0.07177 Biso 1.000 O
+ O54 1.0000 0.41310 0.01887 0.64767 Biso 1.000 O
diff --git a/benchmarks/mof/structures/general/HKUST-1.cif b/benchmarks/mof/structures/general/HKUST-1.cif
new file mode 100644
index 0000000000000000000000000000000000000000..d7d2adad361d09830475837f67f22b7ca9a53f4d
--- /dev/null
+++ b/benchmarks/mof/structures/general/HKUST-1.cif
@@ -0,0 +1,180 @@
+data_FIQCEN_clean
+_audit_creation_date 2014-07-02
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 18.6273
+_cell_length_b 18.6273
+_cell_length_c 18.6273
+_cell_angle_alpha 60.0000
+_cell_angle_beta 60.0000
+_cell_angle_gamma 60.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+Cu1 Cu 0.57057 -0.00000 0.42943 0.01267 Uiso 1.00
+Cu2 Cu 0.00000 0.57057 0.00000 0.01267 Uiso 1.00
+Cu3 Cu 0.42943 0.00000 0.57057 0.01267 Uiso 1.00
+Cu4 Cu 0.00000 0.42943 0.00000 0.01267 Uiso 1.00
+Cu5 Cu 0.57057 0.42943 -0.00000 0.01267 Uiso 1.00
+Cu6 Cu 0.00000 1.00000 0.42943 0.01267 Uiso 1.00
+Cu7 Cu 0.42943 0.57057 -0.00000 0.01267 Uiso 1.00
+Cu8 Cu 0.00000 0.00000 0.57057 0.01267 Uiso 1.00
+Cu9 Cu 0.57057 1.00000 -0.00000 0.01267 Uiso 1.00
+Cu10 Cu 0.00000 0.42943 0.57057 0.01267 Uiso 1.00
+Cu11 Cu 0.42943 1.00000 0.00000 0.01267 Uiso 1.00
+Cu12 Cu 0.00000 0.57057 0.42943 0.01267 Uiso 1.00
+H1 H 0.72800 0.72800 0.51160 0.01267 Uiso 1.00
+H2 H 0.72800 0.72800 0.03240 0.01267 Uiso 1.00
+H3 H 0.51160 0.03240 0.72800 0.01267 Uiso 1.00
+H4 H 0.03240 0.51160 0.72800 0.01267 Uiso 1.00
+H5 H 0.72800 0.51160 0.03240 0.01267 Uiso 1.00
+H6 H 0.72800 0.03240 0.51160 0.01267 Uiso 1.00
+H7 H 0.51160 0.72800 0.72800 0.01267 Uiso 1.00
+H8 H 0.03240 0.72800 0.72800 0.01267 Uiso 1.00
+H9 H 0.72800 0.03240 0.72800 0.01267 Uiso 1.00
+H10 H 0.72800 0.51160 0.72800 0.01267 Uiso 1.00
+H11 H 0.51160 0.72800 0.03240 0.01267 Uiso 1.00
+H12 H 0.03240 0.72800 0.51160 0.01267 Uiso 1.00
+H13 H 0.27200 0.27200 0.48840 0.01267 Uiso 1.00
+H14 H 0.27200 0.27200 0.96760 0.01267 Uiso 1.00
+H15 H 0.96760 0.48840 0.27200 0.01267 Uiso 1.00
+H16 H 0.48840 0.96760 0.27200 0.01267 Uiso 1.00
+H17 H 0.48840 0.27200 0.96760 0.01267 Uiso 1.00
+H18 H 0.96760 0.27200 0.48840 0.01267 Uiso 1.00
+H19 H 0.27200 0.48840 0.27200 0.01267 Uiso 1.00
+H20 H 0.27200 0.96760 0.27200 0.01267 Uiso 1.00
+H21 H 0.96760 0.27200 0.27200 0.01267 Uiso 1.00
+H22 H 0.48840 0.27200 0.27200 0.01267 Uiso 1.00
+H23 H 0.27200 0.48840 0.96760 0.01267 Uiso 1.00
+H24 H 0.27200 0.96760 0.48840 0.01267 Uiso 1.00
+C1 C 0.25700 0.96900 0.38700 0.01267 Uiso 1.00
+C2 C 0.96900 0.25700 0.38700 0.01267 Uiso 1.00
+C3 C 0.38700 0.38700 0.25700 0.01267 Uiso 1.00
+C4 C 0.38700 0.38700 0.96900 0.01267 Uiso 1.00
+C5 C 0.25700 0.38700 0.38700 0.01267 Uiso 1.00
+C6 C 0.96900 0.38700 0.38700 0.01267 Uiso 1.00
+C7 C 0.38700 0.25700 0.96900 0.01267 Uiso 1.00
+C8 C 0.38700 0.96900 0.25700 0.01267 Uiso 1.00
+C9 C 0.25700 0.38700 0.96900 0.01267 Uiso 1.00
+C10 C 0.96900 0.38700 0.25700 0.01267 Uiso 1.00
+C11 C 0.38700 0.96900 0.38700 0.01267 Uiso 1.00
+C12 C 0.38700 0.25700 0.38700 0.01267 Uiso 1.00
+C13 C 0.03100 0.74300 0.61300 0.01267 Uiso 1.00
+C14 C 0.74300 0.03100 0.61300 0.01267 Uiso 1.00
+C15 C 0.61300 0.61300 0.74300 0.01267 Uiso 1.00
+C16 C 0.61300 0.61300 0.03100 0.01267 Uiso 1.00
+C17 C 0.61300 0.74300 0.61300 0.01267 Uiso 1.00
+C18 C 0.61300 0.03100 0.61300 0.01267 Uiso 1.00
+C19 C 0.74300 0.61300 0.03100 0.01267 Uiso 1.00
+C20 C 0.03100 0.61300 0.74300 0.01267 Uiso 1.00
+C21 C 0.61300 0.74300 0.03100 0.01267 Uiso 1.00
+C22 C 0.61300 0.03100 0.74300 0.01267 Uiso 1.00
+C23 C 0.03100 0.61300 0.61300 0.01267 Uiso 1.00
+C24 C 0.74300 0.61300 0.61300 0.01267 Uiso 1.00
+C25 C 0.56870 0.56870 0.83770 0.01267 Uiso 1.00
+C26 C 0.56870 0.56870 0.02490 0.01267 Uiso 1.00
+C27 C 0.83770 0.02490 0.56870 0.01267 Uiso 1.00
+C28 C 0.02490 0.83770 0.56870 0.01267 Uiso 1.00
+C29 C 0.56870 0.83770 0.02490 0.01267 Uiso 1.00
+C30 C 0.56870 0.02490 0.83770 0.01267 Uiso 1.00
+C31 C 0.83770 0.56870 0.56870 0.01267 Uiso 1.00
+C32 C 0.02490 0.56870 0.56870 0.01267 Uiso 1.00
+C33 C 0.56870 0.02490 0.56870 0.01267 Uiso 1.00
+C34 C 0.56870 0.83770 0.56870 0.01267 Uiso 1.00
+C35 C 0.83770 0.56870 0.02490 0.01267 Uiso 1.00
+C36 C 0.02490 0.56870 0.83770 0.01267 Uiso 1.00
+C37 C 0.43130 0.43130 0.16230 0.01267 Uiso 1.00
+C38 C 0.43130 0.43130 0.97510 0.01267 Uiso 1.00
+C39 C 0.97510 0.16230 0.43130 0.01267 Uiso 1.00
+C40 C 0.16230 0.97510 0.43130 0.01267 Uiso 1.00
+C41 C 0.16230 0.43130 0.97510 0.01267 Uiso 1.00
+C42 C 0.97510 0.43130 0.16230 0.01267 Uiso 1.00
+C43 C 0.43130 0.16230 0.43130 0.01267 Uiso 1.00
+C44 C 0.43130 0.97510 0.43130 0.01267 Uiso 1.00
+C45 C 0.97510 0.43130 0.43130 0.01267 Uiso 1.00
+C46 C 0.16230 0.43130 0.43130 0.01267 Uiso 1.00
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diff --git a/benchmarks/mof/structures/general/MIL53(Al).cif b/benchmarks/mof/structures/general/MIL53(Al).cif
new file mode 100644
index 0000000000000000000000000000000000000000..6467a56520f2d1359288706c78546576abf42146
--- /dev/null
+++ b/benchmarks/mof/structures/general/MIL53(Al).cif
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diff --git a/benchmarks/mof/structures/general/MOF-177.cif b/benchmarks/mof/structures/general/MOF-177.cif
new file mode 100644
index 0000000000000000000000000000000000000000..f9273ba9801dbfc5696e88ef3e6aec851e5d2ed1
--- /dev/null
+++ b/benchmarks/mof/structures/general/MOF-177.cif
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+C791 C 0.69737 0.25220 0.72225 0.00000 Uiso 1.00
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+C801 C 0.69542 0.44783 0.72695 0.00000 Uiso 1.00
+C802 C 0.71734 0.49152 0.72770 0.00000 Uiso 1.00
+X803 C 0.75649 0.51354 0.74844 0.00000 Uiso 1.00
+C804 C 0.77299 0.49082 0.76882 0.00000 Uiso 1.00
+H805 H 0.76401 0.43098 0.78629 0.00000 Uiso 1.00
+H806 H 0.66600 0.43220 0.70942 0.00000 Uiso 1.00
+H807 H 0.70394 0.50812 0.71134 0.00000 Uiso 1.00
+H808 H 0.80289 0.50685 0.78547 0.00000 Uiso 1.00
+loop_
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+_geom_bond_distance
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+X1 C2 1.407 . A
+X1 C6 1.410 . A
+X1 X450 1.496 1_655 S
+C2 X3 1.407 . A
+C2 H7 1.084 . S
+X3 C4 1.410 . A
+X3 X470 1.496 . S
+C4 X5 1.419 . A
+C4 H8 1.082 . S
+X5 C6 1.419 . A
+X5 X460 1.433 1_665 A
+C6 H9 1.083 . S
+X10 C11 1.420 . A
+X10 C15 1.415 . A
+X10 X480 1.435 1_665 A
+C11 X12 1.424 . A
+C11 H16 1.080 . S
+X12 C13 1.420 . A
+X12 X490 1.442 1_565 A
+C13 X14 1.408 . A
+C13 H17 1.081 . S
+X14 C15 1.406 . A
+X14 X500 1.499 . S
+C15 H18 1.083 . S
+X19 C20 1.410 . A
+X19 C24 1.410 . A
+X19 X750 1.503 . S
+C20 X21 1.410 . A
+C20 H25 1.081 . S
+X21 C22 1.410 . A
+X21 X760 1.503 . S
+C22 X23 1.410 . A
+C22 H26 1.081 . S
+X23 C24 1.410 . A
+X23 X770 1.503 . S
+C24 H27 1.081 . S
+X28 C29 1.413 . A
+X28 C33 1.410 . A
+X28 X780 1.504 . S
+C29 X30 1.412 . A
+C29 H34 1.081 . S
+X30 C31 1.410 . A
+X30 X800 1.504 . S
+C31 X32 1.407 . A
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+X32 C33 1.407 . A
+X32 X790 1.495 . S
+C33 H36 1.082 . S
+X37 C38 1.418 . A
+X37 C42 1.413 . A
+X37 X510 1.438 1_556 A
+C38 X39 1.426 . A
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+X39 C40 1.422 . A
+X39 X330 1.445 1_556 A
+C40 X41 1.411 . A
+C40 H44 1.080 . S
+X41 C42 1.402 . A
+X41 X650 1.499 . S
+C42 H45 1.081 . S
+X46 C47 1.409 . A
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+C47 X48 1.412 . A
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+X48 C49 1.413 . A
+X48 X340 1.499 . S
+C49 X50 1.412 . A
+C49 H53 1.083 . S
+X50 C51 1.411 . A
+X50 X530 1.502 1_545 S
+C51 H54 1.083 . S
+X55 C56 1.417 . A
+X55 C60 1.406 . A
+X55 X670 1.507 . S
+C56 X57 1.422 . A
+C56 H61 1.079 . S
+X57 C58 1.423 . A
+X57 X350 1.444 1_556 A
+C58 X59 1.409 . A
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+X59 C60 1.407 . A
+X59 X540 1.504 1_546 S
+C60 H63 1.082 . S
+X64 C65 1.410 . A
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+X64 X550 1.504 . S
+C65 X66 1.417 . A
+C65 H70 1.081 . S
+X66 C67 1.416 . A
+X66 X360 1.511 . S
+C67 X68 1.413 . A
+C67 H71 1.082 . S
+X68 C69 1.406 . A
+X68 X640 1.498 . S
+C69 H72 1.083 . S
+X73 C74 1.405 . A
+X73 C78 1.410 . A
+X73 X690 1.499 . S
+C74 X75 1.417 . A
+C74 H79 1.081 . S
+X75 C76 1.420 . A
+X75 X580 1.439 1_455 A
+C76 X77 1.420 . A
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+X77 C78 1.414 . A
+X77 X400 1.515 . S
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+X82 C83 1.408 . A
+X82 C87 1.407 . A
+X82 X630 1.497 . S
+C83 X84 1.409 . A
+C83 H88 1.084 . S
+X84 C85 1.412 . A
+X84 X410 1.500 1_554 S
+C85 X86 1.418 . A
+C85 H89 1.082 . S
+X86 C87 1.418 . A
+X86 X590 1.432 1_454 A
+C87 H90 1.082 . S
+X91 C92 1.408 . A
+X91 C96 1.409 . A
+X91 X700 1.505 . S
+C92 X93 1.421 . A
+C92 H97 1.079 . S
+X93 C94 1.420 . A
+X93 X520 1.437 1_655 A
+C94 X95 1.412 . A
+C94 H98 1.081 . S
+X95 C96 1.407 . A
+X95 X370 1.500 . S
+C96 H99 1.083 . S
+X100 C101 1.412 . A
+X100 C105 1.411 . A
+X100 X600 1.502 . S
+C101 X102 1.413 . A
+C101 H106 1.083 . S
+X102 C103 1.412 . A
+X102 X380 1.499 . S
+C103 X104 1.410 . A
+C103 H107 1.083 . S
+X104 C105 1.405 . A
+X104 X710 1.496 . S
+C105 H108 1.083 . S
+X109 C110 1.413 . A
+X109 C114 1.403 . A
+X109 X720 1.498 . S
+C110 X111 1.423 . A
+C110 H115 1.080 . S
+X111 C112 1.425 . A
+X111 X390 1.443 1_556 A
+C112 X113 1.409 . A
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+X113 C114 1.405 . A
+X113 X570 1.504 1_656 S
+C114 H117 1.083 . S
+X118 C119 1.411 . A
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+X118 X740 1.499 . S
+C119 X120 1.413 . A
+C119 H124 1.083 . S
+X120 C121 1.416 . A
+X120 X420 1.511 . S
+C121 X122 1.416 . A
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+X122 C123 1.415 . A
+X122 X610 1.433 1_565 A
+C123 H126 1.082 . S
+X127 C128 1.405 . A
+X127 C132 1.406 . A
+X127 X680 1.497 . S
+C128 X129 1.419 . A
+C128 H133 1.081 . S
+X129 C130 1.421 . A
+X129 X560 1.437 1_564 A
+C130 X131 1.415 . A
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+X131 C132 1.410 . A
+X131 X430 1.502 1_554 S
+C132 H135 1.083 . S
+X136 C137 1.421 . A
+X136 C141 1.417 . A
+X136 X620 1.438 1_554 A
+C137 X138 1.423 . A
+C137 H142 1.080 . S
+X138 C139 1.418 . A
+X138 X440 1.436 1_554 A
+C139 X140 1.406 . A
+C139 H143 1.082 . S
+X140 C141 1.402 . A
+X140 X730 1.497 . S
+C141 H144 1.081 . S
+Zn145 O149 1.833 . S
+Zn145 O150 1.791 . S
+Zn145 O154 1.822 . S
+Zn145 O161 1.810 . S
+Zn146 O149 1.868 . S
+Zn146 O151 1.851 . S
+Zn146 O153 1.849 . S
+Zn146 O157 1.849 . S
+Zn147 O149 1.832 . S
+Zn147 O152 1.810 . S
+Zn147 O155 1.786 . S
+Zn147 O158 1.819 . S
+Zn148 O149 1.833 . S
+Zn148 O156 1.812 . S
+Zn148 O159 1.788 . S
+Zn148 O160 1.821 . S
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+O151 X163 1.280 . A
+O152 X164 1.273 . A
+O153 X162 1.280 . A
+O154 X164 1.274 . A
+O155 X163 1.271 . A
+O156 X165 1.273 . A
+O157 X166 1.280 . A
+O158 X165 1.274 . A
+O159 X166 1.271 . A
+O160 X167 1.274 . A
+O161 X167 1.273 . A
+X162 X333 1.502 . S
+X163 X393 1.502 . S
+X164 X383 1.497 . S
+X165 X373 1.497 . S
+X166 X353 1.502 . S
+X167 X343 1.497 . S
+Zn168 O172 1.833 . S
+Zn168 O173 1.826 . S
+Zn168 O177 1.794 . S
+Zn168 O184 1.813 . S
+Zn169 O172 1.831 . S
+Zn169 O174 1.789 . S
+Zn169 O176 1.807 . S
+Zn169 O180 1.822 . S
+Zn170 O172 1.866 . S
+Zn170 O175 1.851 . S
+Zn170 O178 1.849 . S
+Zn170 O181 1.848 . S
+Zn171 O172 1.833 . S
+Zn171 O179 1.791 . S
+Zn171 O182 1.813 . S
+Zn171 O183 1.824 . S
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+O174 X186 1.271 . A
+O175 X187 1.280 . A
+O176 X185 1.273 . A
+O177 X187 1.272 . A
+O178 X186 1.279 . A
+O179 X188 1.272 . A
+O180 X189 1.274 . A
+O181 X188 1.280 . A
+O182 X189 1.273 . A
+O183 X190 1.275 . A
+O184 X190 1.274 . A
+X185 X413 1.497 . S
+X186 X363 1.499 . S
+X187 X403 1.502 . S
+X188 X423 1.499 . S
+X189 X443 1.495 . S
+X190 X433 1.499 . S
+Zn191 O195 1.834 . S
+Zn191 O196 1.790 . S
+Zn191 O200 1.823 . S
+Zn191 O207 1.817 . S
+Zn192 O195 1.843 . S
+Zn192 O197 1.803 . S
+Zn192 O199 1.834 . S
+Zn192 O203 1.805 . S
+Zn193 O195 1.844 . S
+Zn193 O198 1.806 . S
+Zn193 O201 1.807 . S
+Zn193 O204 1.835 . S
+Zn194 O195 1.833 . S
+Zn194 O202 1.788 . S
+Zn194 O205 1.824 . S
+Zn194 O206 1.818 . S
+O196 X208 1.270 . A
+O197 X209 1.273 . A
+O198 X210 1.273 . A
+O199 X208 1.277 . A
+O200 X210 1.275 . A
+O201 X209 1.273 . A
+O202 X211 1.271 . A
+O203 X212 1.272 . A
+O204 X211 1.276 . A
+O205 X212 1.275 . A
+O206 X213 1.275 . A
+O207 X213 1.274 . A
+X208 X513 1.497 . S
+X209 X753 1.487 . S
+X210 X633 1.496 . S
+X211 X523 1.497 . S
+X212 X643 1.496 . S
+X213 X463 1.490 . S
+Zn214 O218 1.833 . S
+Zn214 O219 1.825 . S
+Zn214 O223 1.792 . S
+Zn214 O230 1.822 . S
+Zn215 O218 1.842 . S
+Zn215 O220 1.803 . S
+Zn215 O222 1.805 . S
+Zn215 O226 1.835 . S
+Zn216 O218 1.843 . S
+Zn216 O221 1.836 . S
+Zn216 O224 1.809 . S
+Zn216 O227 1.802 . S
+Zn217 O218 1.834 . S
+Zn217 O225 1.824 . S
+Zn217 O228 1.791 . S
+Zn217 O229 1.818 . S
+O219 X231 1.276 . A
+O220 X232 1.273 . A
+O221 X233 1.276 . A
+O222 X231 1.272 . A
+O223 X233 1.271 . A
+O224 X232 1.274 . A
+O225 X234 1.276 . A
+O226 X235 1.277 . A
+O227 X234 1.272 . A
+O228 X235 1.271 . A
+O229 X236 1.275 . A
+O230 X236 1.276 . A
+X231 X653 1.496 . S
+X232 X783 1.488 . S
+X233 X563 1.498 . S
+X234 X663 1.495 . S
+X235 X553 1.497 . S
+X236 X483 1.492 . S
+Zn237 O241 1.832 . S
+Zn237 O242 1.790 . S
+Zn237 O246 1.825 . S
+Zn237 O253 1.819 . S
+Zn238 O241 1.841 . S
+Zn238 O243 1.805 . S
+Zn238 O245 1.832 . S
+Zn238 O249 1.804 . S
+Zn239 O241 1.841 . S
+Zn239 O244 1.808 . S
+Zn239 O247 1.804 . S
+Zn239 O250 1.834 . S
+Zn240 O241 1.832 . S
+Zn240 O248 1.791 . S
+Zn240 O251 1.823 . S
+Zn240 O252 1.817 . S
+O242 X254 1.271 . A
+O243 X255 1.273 . A
+O244 X256 1.273 . A
+O245 X254 1.276 . A
+O246 X256 1.276 . A
+O247 X255 1.273 . A
+O248 X257 1.271 . A
+O249 X258 1.272 . A
+O250 X257 1.277 . A
+O251 X258 1.275 . A
+O252 X259 1.275 . A
+O253 X259 1.275 . A
+X254 X533 1.495 . S
+X255 X763 1.487 . S
+X256 X693 1.497 . S
+X257 X573 1.496 . S
+X258 X683 1.495 . S
+X259 X453 1.495 . S
+Zn260 O264 1.832 . S
+Zn260 O265 1.790 . S
+Zn260 O269 1.822 . S
+Zn260 O276 1.816 . S
+Zn261 O264 1.842 . S
+Zn261 O266 1.805 . S
+Zn261 O268 1.833 . S
+Zn261 O272 1.804 . S
+Zn262 O264 1.842 . S
+Zn262 O267 1.810 . S
+Zn262 O270 1.805 . S
+Zn262 O273 1.834 . S
+Zn263 O264 1.834 . S
+Zn263 O271 1.791 . S
+Zn263 O274 1.823 . S
+Zn263 O275 1.818 . S
+O265 X277 1.271 . A
+O266 X278 1.273 . A
+O267 X279 1.273 . A
+O268 X277 1.276 . A
+O269 X279 1.275 . A
+O270 X278 1.273 . A
+O271 X280 1.271 . A
+O272 X281 1.272 . A
+O273 X280 1.277 . A
+O274 X281 1.275 . A
+O275 X282 1.275 . A
+O276 X282 1.275 . A
+X277 X603 1.495 . S
+X278 X773 1.487 . S
+X279 X743 1.497 . S
+X280 X543 1.496 . S
+X281 X733 1.495 . S
+X282 X473 1.495 . S
+Zn283 O287 1.816 . S
+Zn283 O288 1.793 . S
+Zn283 O292 1.781 . S
+Zn283 O299 1.807 . S
+Zn284 O287 1.843 . S
+Zn284 O289 1.828 . S
+Zn284 O291 1.833 . S
+Zn284 O295 1.850 . S
+Zn285 O287 1.842 . S
+Zn285 O290 1.845 . S
+Zn285 O293 1.823 . S
+Zn285 O296 1.830 . S
+Zn286 O287 1.816 . S
+Zn286 O294 1.794 . S
+Zn286 O297 1.781 . S
+Zn286 O298 1.807 . S
+O288 X300 1.270 . A
+O289 X301 1.280 . A
+O290 X302 1.279 . A
+O291 X300 1.275 . A
+O292 X302 1.270 . A
+O293 X301 1.279 . A
+O294 X303 1.270 . A
+O295 X304 1.280 . A
+O296 X303 1.275 . A
+O297 X304 1.270 . A
+O298 X305 1.273 . A
+O299 X305 1.273 . A
+X300 X613 1.492 . S
+X301 X493 1.503 . S
+X302 X703 1.495 . S
+X303 X593 1.491 . S
+X304 X673 1.497 . S
+X305 X793 1.491 . S
+Zn306 O310 1.834 . S
+Zn306 O311 1.825 . S
+Zn306 O315 1.796 . S
+Zn306 O322 1.821 . S
+Zn307 O310 1.842 . S
+Zn307 O312 1.806 . S
+Zn307 O314 1.801 . S
+Zn307 O318 1.835 . S
+Zn308 O310 1.842 . S
+Zn308 O313 1.837 . S
+Zn308 O316 1.807 . S
+Zn308 O319 1.806 . S
+Zn309 O310 1.832 . S
+Zn309 O317 1.823 . S
+Zn309 O320 1.791 . S
+Zn309 O321 1.819 . S
+O311 X323 1.276 . A
+O312 X324 1.273 . A
+O313 X325 1.277 . A
+O314 X323 1.272 . A
+O315 X325 1.272 . A
+O316 X324 1.273 . A
+O317 X326 1.275 . A
+O318 X327 1.276 . A
+O319 X326 1.273 . A
+O320 X327 1.271 . A
+O321 X328 1.275 . A
+O322 X328 1.276 . A
+X323 X713 1.495 . S
+X324 X803 1.488 . S
+X325 X583 1.500 . S
+X326 X723 1.496 . S
+X327 X623 1.498 . S
+X328 X503 1.497 . S
+C329 X330 1.417 . A
+C329 C334 1.399 . A
+C329 H335 1.079 . S
+X330 C331 1.422 . A
+X330 X39 1.445 1_554 A
+C331 C332 1.414 . A
+C331 H336 1.080 . S
+C332 X333 1.406 . A
+C332 H337 1.083 . S
+X333 C334 1.405 . A
+C334 H338 1.083 . S
+C339 X340 1.408 . A
+C339 C344 1.407 . A
+C339 H345 1.083 . S
+X340 C341 1.407 . A
+C341 C342 1.404 . A
+C341 H346 1.083 . S
+C342 X343 1.408 . A
+C342 H347 1.083 . S
+X343 C344 1.409 . A
+C344 H348 1.083 . S
+C349 X350 1.423 . A
+C349 C354 1.413 . A
+C349 H355 1.080 . S
+X350 C351 1.416 . A
+X350 X57 1.444 1_554 A
+C351 C352 1.399 . A
+C351 H356 1.079 . S
+C352 X353 1.406 . A
+C352 H357 1.083 . S
+X353 C354 1.405 . A
+C354 H358 1.083 . S
+C359 X360 1.408 . A
+C359 C364 1.399 . A
+C359 H365 1.082 . S
+X360 C361 1.415 . A
+C361 C362 1.413 . A
+C361 H366 1.082 . S
+C362 X363 1.408 . A
+C362 H367 1.083 . S
+X363 C364 1.408 . A
+C364 H368 1.083 . S
+C369 X370 1.408 . A
+C369 C374 1.404 . A
+C369 H375 1.083 . S
+X370 C371 1.408 . A
+C371 C372 1.407 . A
+C371 H376 1.083 . S
+C372 X373 1.409 . A
+C372 H377 1.083 . S
+X373 C374 1.408 . A
+C374 H378 1.083 . S
+C379 X380 1.408 . A
+C379 C384 1.407 . A
+C379 H385 1.083 . S
+X380 C381 1.407 . A
+C381 C382 1.404 . A
+C381 H386 1.083 . S
+C382 X383 1.408 . A
+C382 H387 1.083 . S
+X383 C384 1.408 . A
+C384 H388 1.083 . S
+C389 X390 1.416 . A
+C389 C394 1.398 . A
+C389 H395 1.079 . S
+X390 C391 1.422 . A
+X390 X111 1.443 1_554 A
+C391 C392 1.414 . A
+C391 H396 1.080 . S
+C392 X393 1.406 . A
+C392 H397 1.083 . S
+X393 C394 1.406 . A
+C394 H398 1.083 . S
+C399 X400 1.415 . A
+C399 C404 1.414 . A
+C399 H405 1.082 . S
+X400 C401 1.409 . A
+C401 C402 1.401 . A
+C401 H406 1.082 . S
+C402 X403 1.408 . A
+C402 H407 1.083 . S
+X403 C404 1.408 . A
+C404 H408 1.083 . S
+C409 X410 1.407 . A
+C409 C414 1.405 . A
+C409 H415 1.083 . S
+X410 C411 1.408 . A
+X410 X84 1.500 1_556 S
+C411 C412 1.407 . A
+C411 H416 1.083 . S
+C412 X413 1.408 . A
+C412 H417 1.083 . S
+X413 C414 1.409 . A
+C414 H418 1.083 . S
+C419 X420 1.408 . A
+C419 C424 1.399 . A
+C419 H425 1.082 . S
+X420 C421 1.415 . A
+C421 C422 1.413 . A
+C421 H426 1.082 . S
+C422 X423 1.408 . A
+C422 H427 1.083 . S
+X423 C424 1.408 . A
+C424 H428 1.083 . S
+C429 X430 1.408 . A
+C429 C434 1.408 . A
+C429 H435 1.083 . S
+X430 C431 1.408 . A
+X430 X131 1.502 1_556 S
+C431 C432 1.405 . A
+C431 H436 1.083 . S
+C432 X433 1.408 . A
+C432 H437 1.083 . S
+X433 C434 1.409 . A
+C434 H438 1.083 . S
+C439 X440 1.416 . A
+C439 C444 1.407 . A
+C439 H445 1.082 . S
+X440 C441 1.417 . A
+X440 X138 1.436 1_556 A
+C441 C442 1.405 . A
+C441 H446 1.082 . S
+C442 X443 1.404 . A
+C442 H447 1.083 . S
+X443 C444 1.405 . A
+C444 H448 1.083 . S
+C449 X450 1.407 . A
+C449 C454 1.406 . A
+C449 H455 1.083 . S
+X450 C451 1.407 . A
+X450 X1 1.496 1_455 S
+C451 C452 1.406 . A
+C451 H456 1.083 . S
+C452 X453 1.408 . A
+C452 H457 1.083 . S
+X453 C454 1.408 . A
+C454 H458 1.083 . S
+C459 X460 1.416 . A
+C459 C464 1.406 . A
+C459 H465 1.082 . S
+X460 C461 1.416 . A
+X460 X5 1.433 1_445 A
+C461 C462 1.406 . A
+C461 H466 1.082 . S
+C462 X463 1.403 . A
+C462 H467 1.083 . S
+X463 C464 1.404 . A
+C464 H468 1.083 . S
+C469 X470 1.407 . A
+C469 C474 1.406 . A
+C469 H475 1.083 . S
+X470 C471 1.406 . A
+C471 C472 1.406 . A
+C471 H476 1.083 . S
+C472 X473 1.408 . A
+C472 H477 1.083 . S
+X473 C474 1.408 . A
+C474 H478 1.083 . S
+C479 X480 1.416 . A
+C479 C484 1.407 . A
+C479 H485 1.082 . S
+X480 C481 1.417 . A
+X480 X10 1.435 1_445 A
+C481 C482 1.406 . A
+C481 H486 1.082 . S
+C482 X483 1.403 . A
+C482 H487 1.083 . S
+X483 C484 1.405 . A
+C484 H488 1.083 . S
+C489 X490 1.418 . A
+C489 C494 1.407 . A
+C489 H495 1.081 . S
+X490 C491 1.418 . A
+X490 X12 1.442 1_545 A
+C491 C492 1.407 . A
+C491 H496 1.081 . S
+C492 X493 1.407 . A
+C492 H497 1.083 . S
+X493 C494 1.406 . A
+C494 H498 1.083 . S
+C499 X500 1.407 . A
+C499 C504 1.406 . A
+C499 H505 1.083 . S
+X500 C501 1.407 . A
+C501 C502 1.407 . A
+C501 H506 1.083 . S
+C502 X503 1.409 . A
+C502 H507 1.083 . S
+X503 C504 1.408 . A
+C504 H508 1.083 . S
+C509 X510 1.420 . A
+C509 C514 1.409 . A
+C509 H515 1.080 . S
+X510 C511 1.415 . A
+X510 X37 1.438 1_554 A
+C511 C512 1.398 . A
+C511 H516 1.081 . S
+C512 X513 1.405 . A
+C512 H517 1.083 . S
+X513 C514 1.405 . A
+C514 H518 1.083 . S
+C519 X520 1.419 . A
+C519 C524 1.411 . A
+C519 H525 1.081 . S
+X520 C521 1.415 . A
+X520 X93 1.437 1_455 A
+C521 C522 1.397 . A
+C521 H526 1.080 . S
+C522 X523 1.405 . A
+C522 H527 1.083 . S
+X523 C524 1.406 . A
+C524 H528 1.083 . S
+C529 X530 1.407 . A
+C529 C534 1.398 . A
+C529 H535 1.083 . S
+X530 C531 1.411 . A
+X530 X50 1.502 1_565 S
+C531 C532 1.409 . A
+C531 H536 1.082 . S
+C532 X533 1.408 . A
+C532 H537 1.083 . S
+X533 C534 1.407 . A
+C534 H538 1.083 . S
+C539 X540 1.407 . A
+C539 C544 1.399 . A
+C539 H545 1.083 . S
+X540 C541 1.412 . A
+X540 X59 1.504 1_564 S
+C541 C542 1.409 . A
+C541 H546 1.082 . S
+C542 X543 1.407 . A
+C542 H547 1.083 . S
+X543 C544 1.408 . A
+C544 H548 1.083 . S
+C549 X550 1.412 . A
+C549 C554 1.409 . A
+C549 H555 1.082 . S
+X550 C551 1.407 . A
+C551 C552 1.399 . A
+C551 H556 1.083 . S
+C552 X553 1.408 . A
+C552 H557 1.083 . S
+X553 C554 1.408 . A
+C554 H558 1.083 . S
+C559 X560 1.415 . A
+C559 C564 1.397 . A
+C559 H565 1.081 . S
+X560 C561 1.419 . A
+X560 X129 1.437 1_546 A
+C561 C562 1.411 . A
+C561 H566 1.081 . S
+C562 X563 1.407 . A
+C562 H567 1.083 . S
+X563 C564 1.405 . A
+C564 H568 1.083 . S
+C569 X570 1.412 . A
+C569 C574 1.409 . A
+C569 H575 1.082 . S
+X570 C571 1.407 . A
+X570 X113 1.504 1_454 S
+C571 C572 1.399 . A
+C571 H576 1.083 . S
+C572 X573 1.408 . A
+C572 H577 1.083 . S
+X573 C574 1.407 . A
+C574 H578 1.083 . S
+C579 X580 1.420 . A
+C579 C584 1.411 . A
+C579 H585 1.081 . S
+X580 C581 1.416 . A
+X580 X75 1.439 1_655 A
+C581 C582 1.399 . A
+C581 H586 1.081 . S
+C582 X583 1.405 . A
+C582 H587 1.083 . S
+X583 C584 1.406 . A
+C584 H588 1.083 . S
+C589 X590 1.414 . A
+C589 C594 1.400 . A
+C589 H595 1.082 . S
+X590 C591 1.417 . A
+X590 X86 1.432 1_656 A
+C591 C592 1.409 . A
+C591 H596 1.082 . S
+C592 X593 1.405 . A
+C592 H597 1.083 . S
+X593 C594 1.404 . A
+C594 H598 1.083 . S
+C599 X600 1.411 . A
+C599 C604 1.409 . A
+C599 H605 1.082 . S
+X600 C601 1.407 . A
+C601 C602 1.398 . A
+C601 H606 1.083 . S
+C602 X603 1.408 . A
+C602 H607 1.083 . S
+X603 C604 1.408 . A
+C604 H608 1.083 . S
+C609 X610 1.418 . A
+C609 C614 1.408 . A
+C609 H615 1.082 . S
+X610 C611 1.414 . A
+X610 X122 1.433 1_545 A
+C611 C612 1.401 . A
+C611 H616 1.082 . S
+C612 X613 1.405 . A
+C612 H617 1.083 . S
+X613 C614 1.404 . A
+C614 H618 1.083 . S
+C619 X620 1.415 . A
+C619 C624 1.398 . A
+C619 H625 1.081 . S
+X620 C621 1.419 . A
+X620 X136 1.438 1_556 A
+C621 C622 1.411 . A
+C621 H626 1.081 . S
+C622 X623 1.406 . A
+C622 H627 1.083 . S
+X623 C624 1.405 . A
+C624 H628 1.083 . S
+C629 X630 1.409 . A
+C629 C634 1.407 . A
+C629 H635 1.083 . S
+X630 C631 1.405 . A
+C631 C632 1.404 . A
+C631 H636 1.083 . S
+C632 X633 1.410 . A
+C632 H637 1.083 . S
+X633 C634 1.408 . A
+C634 H638 1.083 . S
+C639 X640 1.405 . A
+C639 C644 1.405 . A
+C639 H645 1.083 . S
+X640 C641 1.409 . A
+C641 C642 1.406 . A
+C641 H646 1.083 . S
+C642 X643 1.407 . A
+C642 H647 1.083 . S
+X643 C644 1.410 . A
+C644 H648 1.083 . S
+C649 X650 1.405 . A
+C649 C654 1.405 . A
+C649 H655 1.083 . S
+X650 C651 1.410 . A
+C651 C652 1.406 . A
+C651 H656 1.083 . S
+C652 X653 1.407 . A
+C652 H657 1.083 . S
+X653 C654 1.411 . A
+C654 H658 1.083 . S
+C659 X660 1.405 . A
+C659 C664 1.403 . A
+C659 H665 1.083 . S
+X660 C661 1.409 . A
+C661 C662 1.406 . A
+C661 H666 1.083 . S
+C662 X663 1.407 . A
+C662 H667 1.083 . S
+X663 C664 1.410 . A
+C664 H668 1.083 . S
+C669 X670 1.406 . A
+C669 C674 1.400 . A
+C669 H675 1.082 . S
+X670 C671 1.414 . A
+C671 C672 1.410 . A
+C671 H676 1.082 . S
+C672 X673 1.406 . A
+C672 H677 1.083 . S
+X673 C674 1.410 . A
+C674 H678 1.083 . S
+C679 X680 1.405 . A
+C679 C684 1.404 . A
+C679 H685 1.083 . S
+X680 C681 1.409 . A
+C681 C682 1.406 . A
+C681 H686 1.083 . S
+C682 X683 1.407 . A
+C682 H687 1.083 . S
+X683 C684 1.410 . A
+C684 H688 1.083 . S
+C689 X690 1.405 . A
+C689 C694 1.406 . A
+C689 H695 1.083 . S
+X690 C691 1.409 . A
+C691 C692 1.406 . A
+C691 H696 1.083 . S
+C692 X693 1.407 . A
+C692 H697 1.083 . S
+X693 C694 1.411 . A
+C694 H698 1.083 . S
+C699 X700 1.406 . A
+C699 C704 1.399 . A
+C699 H705 1.082 . S
+X700 C701 1.414 . A
+C701 C702 1.409 . A
+C701 H706 1.082 . S
+C702 X703 1.406 . A
+C702 H707 1.083 . S
+X703 C704 1.409 . A
+C704 H708 1.083 . S
+C709 X710 1.405 . A
+C709 C714 1.403 . A
+C709 H715 1.083 . S
+X710 C711 1.409 . A
+C711 C712 1.406 . A
+C711 H716 1.083 . S
+C712 X713 1.407 . A
+C712 H717 1.083 . S
+X713 C714 1.410 . A
+C714 H718 1.083 . S
+C719 X720 1.409 . A
+C719 C724 1.405 . A
+C719 H725 1.083 . S
+X720 C721 1.405 . A
+C721 C722 1.405 . A
+C721 H726 1.083 . S
+C722 X723 1.410 . A
+C722 H727 1.083 . S
+X723 C724 1.407 . A
+C724 H728 1.083 . S
+C729 X730 1.409 . A
+C729 C734 1.406 . A
+C729 H735 1.083 . S
+X730 C731 1.405 . A
+C731 C732 1.404 . A
+C731 H736 1.083 . S
+C732 X733 1.410 . A
+C732 H737 1.083 . S
+X733 C734 1.407 . A
+C734 H738 1.083 . S
+C739 X740 1.409 . A
+C739 C744 1.406 . A
+C739 H745 1.083 . S
+X740 C741 1.405 . A
+C741 C742 1.405 . A
+C741 H746 1.083 . S
+C742 X743 1.410 . A
+C742 H747 1.083 . S
+X743 C744 1.407 . A
+C744 H748 1.083 . S
+C749 X750 1.410 . A
+C749 C754 1.402 . A
+C749 H755 1.082 . S
+X750 C751 1.410 . A
+C751 C752 1.402 . A
+C751 H756 1.082 . S
+C752 X753 1.405 . A
+C752 H757 1.083 . S
+X753 C754 1.405 . A
+C754 H758 1.083 . S
+C759 X760 1.410 . A
+C759 C764 1.402 . A
+C759 H765 1.082 . S
+X760 C761 1.410 . A
+C761 C762 1.402 . A
+C761 H766 1.082 . S
+C762 X763 1.405 . A
+C762 H767 1.083 . S
+X763 C764 1.405 . A
+C764 H768 1.083 . S
+C769 X770 1.410 . A
+C769 C774 1.402 . A
+C769 H775 1.082 . S
+X770 C771 1.410 . A
+C771 C772 1.402 . A
+C771 H776 1.082 . S
+C772 X773 1.405 . A
+C772 H777 1.083 . S
+X773 C774 1.405 . A
+C774 H778 1.083 . S
+C779 X780 1.411 . A
+C779 C784 1.402 . A
+C779 H785 1.082 . S
+X780 C781 1.410 . A
+C781 C782 1.402 . A
+C781 H786 1.082 . S
+C782 X783 1.406 . A
+C782 H787 1.083 . S
+X783 C784 1.405 . A
+C784 H788 1.083 . S
+C789 X790 1.407 . A
+C789 C794 1.402 . A
+C789 H795 1.083 . S
+X790 C791 1.407 . A
+C791 C792 1.402 . A
+C791 H796 1.083 . S
+C792 X793 1.407 . A
+C792 H797 1.083 . S
+X793 C794 1.407 . A
+C794 H798 1.083 . S
+C799 X800 1.411 . A
+C799 C804 1.402 . A
+C799 H805 1.082 . S
+X800 C801 1.410 . A
+C801 C802 1.403 . A
+C801 H806 1.082 . S
+C802 X803 1.406 . A
+C802 H807 1.083 . S
+X803 C804 1.405 . A
+C804 H808 1.083 . S
diff --git a/benchmarks/mof/structures/general/MOF-5.cif b/benchmarks/mof/structures/general/MOF-5.cif
new file mode 100644
index 0000000000000000000000000000000000000000..f49b1517443dde7d85b82c5e5835d479cc451062
--- /dev/null
+++ b/benchmarks/mof/structures/general/MOF-5.cif
@@ -0,0 +1,130 @@
+data_EDUSIF_clean
+_audit_creation_date 2014-07-02
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
+_symmetry_cell_setting triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+ x,y,z
+_cell_length_a 18.2660
+_cell_length_b 18.2660
+_cell_length_c 18.2660
+_cell_angle_alpha 60.0000
+_cell_angle_beta 60.0000
+_cell_angle_gamma 60.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+Zn1 Zn 0.29338 0.29338 0.29338 0.01267 Uiso 1.00
+Zn2 Zn 0.29338 0.29338 0.11986 0.01267 Uiso 1.00
+Zn3 Zn 0.29338 0.11986 0.29338 0.01267 Uiso 1.00
+Zn4 Zn 0.11986 0.29338 0.29338 0.01267 Uiso 1.00
+Zn5 Zn 0.70662 0.70662 0.70662 0.01267 Uiso 1.00
+Zn6 Zn 0.70662 0.70662 0.88014 0.01267 Uiso 1.00
+Zn7 Zn 0.88014 0.70662 0.70662 0.01267 Uiso 1.00
+Zn8 Zn 0.70662 0.88014 0.70662 0.01267 Uiso 1.00
+H1 H 0.15460 0.45520 0.45520 0.01267 Uiso 1.00
+H2 H 0.45520 0.15460 0.93500 0.01267 Uiso 1.00
+H3 H 0.45520 0.93500 0.15460 0.01267 Uiso 1.00
+H4 H 0.93500 0.45520 0.45520 0.01267 Uiso 1.00
+H5 H 0.15460 0.45520 0.93500 0.01267 Uiso 1.00
+H6 H 0.45520 0.93500 0.45520 0.01267 Uiso 1.00
+H7 H 0.45520 0.15460 0.45520 0.01267 Uiso 1.00
+H8 H 0.93500 0.45520 0.15460 0.01267 Uiso 1.00
+H9 H 0.15460 0.93500 0.45520 0.01267 Uiso 1.00
+H10 H 0.45520 0.45520 0.15460 0.01267 Uiso 1.00
+H11 H 0.45520 0.45520 0.93500 0.01267 Uiso 1.00
+H12 H 0.93500 0.15460 0.45520 0.01267 Uiso 1.00
+H13 H 0.54480 0.84540 0.54480 0.01267 Uiso 1.00
+H14 H 0.84540 0.54480 0.06500 0.01267 Uiso 1.00
+H15 H 0.06500 0.54480 0.84540 0.01267 Uiso 1.00
+H16 H 0.54480 0.06500 0.54480 0.01267 Uiso 1.00
+H17 H 0.54480 0.84540 0.06500 0.01267 Uiso 1.00
+H18 H 0.06500 0.54480 0.54480 0.01267 Uiso 1.00
+H19 H 0.84540 0.54480 0.54480 0.01267 Uiso 1.00
+H20 H 0.54480 0.06500 0.84540 0.01267 Uiso 1.00
+H21 H 0.06500 0.84540 0.54480 0.01267 Uiso 1.00
+H22 H 0.54480 0.54480 0.84540 0.01267 Uiso 1.00
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diff --git a/benchmarks/mof/structures/general/UiO-66.cif b/benchmarks/mof/structures/general/UiO-66.cif
new file mode 100644
index 0000000000000000000000000000000000000000..60010d7dc80d744953041eb301c315922eab83e4
--- /dev/null
+++ b/benchmarks/mof/structures/general/UiO-66.cif
@@ -0,0 +1,138 @@
+data_RUBTAK02_clean_h\(2)
+_audit_creation_date 2015-05-11
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
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+_symmetry_equiv_pos_as_xyz
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+_cell_angle_gamma 60.0000
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+_atom_site_label
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+_atom_site_fract_y
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diff --git a/benchmarks/mof/structures/general/ZIF-8.cif b/benchmarks/mof/structures/general/ZIF-8.cif
new file mode 100644
index 0000000000000000000000000000000000000000..40827d68c164e2d0a42d3bab95593837ad0d53fc
--- /dev/null
+++ b/benchmarks/mof/structures/general/ZIF-8.cif
@@ -0,0 +1,162 @@
+data_OFERUN_clean
+_audit_creation_date 2014-07-02
+_audit_creation_method 'Materials Studio'
+_symmetry_space_group_name_H-M 'P1'
+_symmetry_Int_Tables_number 1
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+_symmetry_equiv_pos_as_xyz
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+_cell_angle_gamma 109.4710
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+_atom_site_label
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+_atom_site_fract_y
+_atom_site_fract_z
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+_atom_site_adp_type
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diff --git a/benchmarks/stability/plot.ipynb b/benchmarks/stability/plot.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..10616336067f4a7d81891134047059f8d34d7652
--- /dev/null
+++ b/benchmarks/stability/plot.ipynb
@@ -0,0 +1,796 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import glob\n",
+ "from pathlib import Path\n",
+ "\n",
+ "from tqdm.auto import tqdm\n",
+ "\n",
+ "from mlip_arena.models import REGISTRY\n",
+ "from mlip_arena.tasks.stability.input import get_atoms_from_db\n",
+ "\n",
+ "RUN_DIR = Path(\".\").resolve()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "compositions = []\n",
+ "sizes = []\n",
+ "for atoms in tqdm(get_atoms_from_db(\"random-mixture.db\")):\n",
+ " if len(atoms) == 0:\n",
+ " continue\n",
+ " compositions.append(atoms.get_chemical_formula())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pymatviz as pmv\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "%matplotlib inline\n",
+ "\n",
+ "fig = pmv.ptable_heatmap(\n",
+ " pmv.count_elements(compositions[:1000]),\n",
+ " colormap=\"GnBu\",\n",
+ " log=True,\n",
+ " return_type=\"figure\",\n",
+ ")\n",
+ "\n",
+ "plt.savefig(\"../figures/stability-element-counts.pdf\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from ase import Atoms\n",
+ "\n",
+ "\n",
+ "def get_runtime_stats(traj: list[Atoms], atoms0: Atoms):\n",
+ " restarts = []\n",
+ " steps, times = [], []\n",
+ " Ts, Ps, Es, KEs = [], [], [], []\n",
+ " timesteps = []\n",
+ " com_drifts = []\n",
+ "\n",
+ " for atoms in tqdm(traj):\n",
+ " assert isinstance(atoms, Atoms)\n",
+ " try:\n",
+ " energy = atoms.get_potential_energy()\n",
+ " assert np.isfinite(energy), f\"invalid energy: {energy}\"\n",
+ " # assert np.all(~np.isnan(atoms.get_forces())), f\"invalid forces: {atoms.get_forces()}\"\n",
+ " # assert np.all(~np.isnan(atoms.get_stress())), f\"invalid stress: {atoms.get_stress()}\"\n",
+ " except Exception:\n",
+ " continue\n",
+ "\n",
+ " restarts.append(atoms.info[\"restart\"])\n",
+ " times.append(atoms.info[\"datetime\"])\n",
+ " steps.append(atoms.info[\"step\"])\n",
+ " Es.append(energy)\n",
+ " KEs.append(atoms.get_kinetic_energy())\n",
+ " Ts.append(atoms.get_temperature())\n",
+ " try:\n",
+ " Ps.append(atoms.get_stress()[:3].mean())\n",
+ " except:\n",
+ " pass\n",
+ " com_drifts.append(\n",
+ " (atoms.get_center_of_mass() - atoms0.get_center_of_mass()).tolist()\n",
+ " )\n",
+ "\n",
+ " restarts = np.array(restarts)\n",
+ " times = np.array(times)\n",
+ " steps = np.array(steps)\n",
+ "\n",
+ " # Identify unique blocks\n",
+ " unique_restarts = np.unique(restarts)\n",
+ "\n",
+ " total_time_seconds = 0\n",
+ " total_steps = 0\n",
+ "\n",
+ " # Iterate over unique blocks to calculate averages\n",
+ " for block in unique_restarts:\n",
+ " # Get the indices corresponding to the current block\n",
+ " # indices = np.where(restarts == block)[0]\n",
+ " indices = restarts == block\n",
+ " # Extract the corresponding data values\n",
+ " block_time = times[indices][-1] - times[indices][0]\n",
+ " total_time_seconds += block_time.total_seconds()\n",
+ " total_steps += steps[indices][-1] - steps[indices][0]\n",
+ "\n",
+ " target_steps = traj[0].info[\"target_steps\"]\n",
+ " natoms = len(traj[0])\n",
+ "\n",
+ " return {\n",
+ " \"natoms\": natoms,\n",
+ " \"total_time_seconds\": total_time_seconds,\n",
+ " \"total_steps\": total_steps,\n",
+ " \"steps_per_second\": total_steps / total_time_seconds\n",
+ " if total_time_seconds != 0\n",
+ " else 0,\n",
+ " \"seconds_per_step\": total_time_seconds / total_steps\n",
+ " if total_steps != 0\n",
+ " else float(\"inf\"),\n",
+ " \"seconds_per_step_per_atom\": total_time_seconds / total_steps / natoms\n",
+ " if total_steps != 0\n",
+ " else float(\"inf\"),\n",
+ " \"energies\": Es,\n",
+ " \"kinetic_energies\": KEs,\n",
+ " \"temperatures\": Ts,\n",
+ " \"pressures\": Ps,\n",
+ " \"target_steps\": target_steps,\n",
+ " \"final_step\": steps[-1] if len(steps) != 0 else 0,\n",
+ " \"timestep\": steps,\n",
+ " \"com_drifts\": com_drifts,\n",
+ " }\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import plotly.colors as pcolors\n",
+ "\n",
+ "mlip_methods = [\n",
+ " model\n",
+ " for model, metadata in REGISTRY.items()\n",
+ " if \"stability\" in metadata.get(\"gpu-tasks\", [])\n",
+ "]\n",
+ "\n",
+ "all_attributes = dir(pcolors.qualitative)\n",
+ "color_palettes = {\n",
+ " attr: getattr(pcolors.qualitative, attr)\n",
+ " for attr in all_attributes\n",
+ " if isinstance(getattr(pcolors.qualitative, attr), list)\n",
+ "}\n",
+ "color_palettes.pop(\"__all__\", None)\n",
+ "\n",
+ "palette_names = list(color_palettes.keys())\n",
+ "palette_colors = list(color_palettes.values())\n",
+ "palette_name = \"T10\" # \"Plotly\"\n",
+ "color_sequence = color_palettes[palette_name] # type: ignore\n",
+ "\n",
+ "method_color_mapping = {\n",
+ " method: color_sequence[i % len(color_sequence)]\n",
+ " for i, method in enumerate(mlip_methods)\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# NPT"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# from huggingface_hub import HfApi\n",
+ "import seaborn as sns\n",
+ "from ase import units\n",
+ "from ase.io import read\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "df = pd.DataFrame()\n",
+ "\n",
+ "for model in mlip_methods:\n",
+ " # if \"stability\" not in REGISTRY[model]['gpu-tasks']:\n",
+ " # continue\n",
+ "\n",
+ " files = glob.glob(str(RUN_DIR / REGISTRY[model][\"family\"] / f\"{model}_*npt.traj\"))\n",
+ "\n",
+ " for i, file in enumerate(files):\n",
+ " try:\n",
+ " traj = read(file, index=\":\")\n",
+ " except Exception as e:\n",
+ " print(f\"Error reading {file}: {e}\")\n",
+ " continue\n",
+ "\n",
+ " try:\n",
+ " stats = get_runtime_stats(traj, atoms0=traj[0])\n",
+ " except Exception as e:\n",
+ " print(f\"Error processing {file}: {e}\")\n",
+ " continue\n",
+ "\n",
+ " df = pd.concat(\n",
+ " [\n",
+ " df,\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " \"model\": model,\n",
+ " \"formula\": traj[0].get_chemical_formula(),\n",
+ " \"normalized_timestep\": stats[\"timestep\"]\n",
+ " / stats[\"target_steps\"],\n",
+ " \"normalized_final_step\": stats[\"final_step\"]\n",
+ " / stats[\"target_steps\"],\n",
+ " \"pressure\": np.array(stats[\"pressures\"]) / units.GPa,\n",
+ " }\n",
+ " | stats\n",
+ " ),\n",
+ " ],\n",
+ " ignore_index=True,\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "# import scipy.optimize as opt\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from scipy.optimize import curve_fit\n",
+ "\n",
+ "\n",
+ "# Define the power-law fitting function\n",
+ "def power_law(x, a, n):\n",
+ " return a * np.power(x, n)\n",
+ "\n",
+ "\n",
+ "df.rename(\n",
+ " columns={\n",
+ " \"final_step\": \"Total steps\",\n",
+ " \"model\": \"Model\",\n",
+ " },\n",
+ " inplace=True,\n",
+ ")\n",
+ "\n",
+ "with plt.style.context(\"default\"):\n",
+ "\n",
+ " SMALL_SIZE = 8\n",
+ "\n",
+ " fig, axes = plt.subplot_mosaic(\n",
+ " \"\"\"\n",
+ " ao\n",
+ " \"\"\",\n",
+ " constrained_layout=True,\n",
+ " figsize=(6, 3),\n",
+ " width_ratios=[1, 3],\n",
+ " )\n",
+ "\n",
+ " iax = \"o\"\n",
+ " ax = axes.pop(iax)\n",
+ "\n",
+ " sns.scatterplot(\n",
+ " data=df,\n",
+ " x=\"natoms\",\n",
+ " y=\"steps_per_second\",\n",
+ " size=\"Total steps\",\n",
+ " hue=\"Model\",\n",
+ " ax=ax,\n",
+ " palette=method_color_mapping,\n",
+ " sizes=(1, 50),\n",
+ " # alpha=0.5\n",
+ " )\n",
+ "\n",
+ " # Fit and plot power-law regression for each model\n",
+ " for model, data in df.groupby(\"Model\"):\n",
+ " data.dropna(subset=[\"steps_per_second\"], inplace=True)\n",
+ "\n",
+ " popt, pcov = curve_fit(power_law, data[\"natoms\"], data[\"steps_per_second\"])\n",
+ "\n",
+ " # Generate smooth curve\n",
+ " # x_fit = np.logspace(np.log10(xdata.min()), np.log10(xdata.max()), 100)\n",
+ " # y_fit = power_law(x_fit, a_fit, n_fit)\n",
+ "\n",
+ " x = np.linspace(data[\"natoms\"].min(), data[\"natoms\"].max(), 100)\n",
+ "\n",
+ " # Plot regression line\n",
+ " ax.plot(\n",
+ " x,\n",
+ " power_law(x, *popt),\n",
+ " c=method_color_mapping[model],\n",
+ " # label=f\"{model} (y={a_fit:.2e}x^{n_fit:.2f})\",\n",
+ " linestyle=\"-\",\n",
+ " )\n",
+ "\n",
+ " # sns.lineplot(\n",
+ " # data=df,\n",
+ " # x='natoms',\n",
+ " # y='steps_per_second',\n",
+ " # # size='Total steps',\n",
+ " # hue='Model',\n",
+ " # ax=ax,\n",
+ " # palette=method_color_mapping,\n",
+ " # alpha=0.5,\n",
+ " # # err_style=\"bars\"\n",
+ " # )\n",
+ "\n",
+ " ax.set(\n",
+ " xlabel=\"Number of atoms\",\n",
+ " xscale=\"log\",\n",
+ " ylabel=\"Steps per second\",\n",
+ " yscale=\"log\",\n",
+ " )\n",
+ " ax.spines[\"right\"].set_visible(False)\n",
+ " ax.spines[\"top\"].set_visible(False)\n",
+ " ax.grid(alpha=0.25)\n",
+ " ax.legend(\n",
+ " loc=\"upper left\", bbox_to_anchor=(1.0, 1.0), fontsize=\"x-small\", frameon=False\n",
+ " )\n",
+ "\n",
+ " fisrt = 80\n",
+ "\n",
+ " for k, df_model in df.groupby(\"Model\"):\n",
+ " ax = axes[\"a\"]\n",
+ "\n",
+ " df_model.drop_duplicates([\"formula\"], inplace=True)\n",
+ " df_model = df_model[df_model[\"formula\"].isin(compositions[:fisrt])].copy()\n",
+ " print(k, len(df_model))\n",
+ "\n",
+ " # Compute histogram\n",
+ " bins = np.linspace(0, 1, 50) # 50 bins from 0 to 1\n",
+ " hist, bin_edges = np.histogram(\n",
+ " df_model[\"normalized_final_step\"], bins=bins, density=False\n",
+ " )\n",
+ "\n",
+ " # Compute cumulative population\n",
+ " cumulative_population = np.cumsum(hist)\n",
+ "\n",
+ " # Midpoints for binning\n",
+ " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
+ "\n",
+ " sns.lineplot(\n",
+ " x=bin_centers[:-1],\n",
+ " y=(cumulative_population[-1] - cumulative_population[:-1]) / first * 100,\n",
+ " ax=axes[\"a\"],\n",
+ " # label=k,\n",
+ " color=method_color_mapping[k],\n",
+ " # palette=method_color_mapping\n",
+ " )\n",
+ "\n",
+ " ax_main = axes[\"a\"]\n",
+ " ax_main.spines[\"right\"].set_visible(False)\n",
+ " ax_temp = ax_main.twiny()\n",
+ " ax_pressure = ax_main.twiny()\n",
+ "\n",
+ " # === Plot styling and range ===\n",
+ " ax_main.set_xlim(0, 1)\n",
+ " ax_main.set_ylim(0, 100)\n",
+ " # ax_main.set_yticks(range(0, 81, 20))\n",
+ " ax_main.set_ylabel(\"valid runs (%)\")\n",
+ "\n",
+ "\n",
+ " # === Set top x-axis: Time (ps) ===\n",
+ " ax_main.set_xticks([0, 1])\n",
+ " ax_main.set_xticklabels([0, 10])\n",
+ " ax_main.set_xlabel(\"Time (ps)\")\n",
+ " ax_main.xaxis.set_label_position(\"top\")\n",
+ " ax_main.xaxis.tick_top()\n",
+ " ax_main.spines[\"top\"].set_position((\"outward\", 5)) # Keep just below plot\n",
+ " # ax_main.tick_params(axis=\"x\", top=True, labeltop=True, bottom=False, labelbottom=False)\n",
+ "\n",
+ " # === Bottom axis: Temperature ===\n",
+ " ax_temp.set_xlim(ax_main.get_xlim())\n",
+ " ax_temp.set_xticks([0, 1])\n",
+ " ax_temp.set_xticklabels([\"300 K\", \"3000 K\"])\n",
+ " # ax_temp.set_xlabel(\"Temperature (K)\")\n",
+ " ax_temp.xaxis.set_ticks_position(\"bottom\")\n",
+ " ax_temp.xaxis.set_label_position(\"bottom\")\n",
+ " ax_temp.spines[\"right\"].set_visible(False)\n",
+ " ax_temp.spines[\"top\"].set_visible(False)\n",
+ " ax_temp.spines[\"bottom\"].set_position((\"outward\", 5)) # Keep just below plot\n",
+ "\n",
+ " # === Lower bottom axis: Pressure ===\n",
+ " ax_pressure.set_xlim(ax_main.get_xlim())\n",
+ " ax_pressure.set_xticks([0, 1])\n",
+ " ax_pressure.set_xticklabels([\"0 GPa\", \"500 GPa\"])\n",
+ " # ax_pressure.set_xlabel(\"Pressure (GPa)\")\n",
+ " ax_pressure.xaxis.set_ticks_position(\"bottom\")\n",
+ " ax_pressure.xaxis.set_label_position(\"bottom\")\n",
+ " ax_pressure.spines[\"right\"].set_visible(False)\n",
+ " ax_pressure.spines[\"top\"].set_visible(False)\n",
+ " ax_pressure.spines[\"bottom\"].set_position((\"outward\", 25)) # Push further down\n",
+ "\n",
+ " # # === Clean up main axis ===\n",
+ " ax_main.legend_ = None\n",
+ "\n",
+ " plt.savefig(\"stability-and-speed-npt-loglog.pdf\", bbox_inches=\"tight\")\n",
+ " plt.savefig(\n",
+ " \"stability-and-speed-npt-loglog.png\", bbox_inches=\"tight\", dpi=330\n",
+ " )\n",
+ "\n",
+ " # plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# NVT"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "# from huggingface_hub import HfApi\n",
+ "import seaborn as sns\n",
+ "from ase import units\n",
+ "from ase.io import read\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "df = pd.DataFrame()\n",
+ "\n",
+ "for model in mlip_methods:\n",
+ " # if \"stability\" not in REGISTRY[model]['gpu-tasks']:\n",
+ " # continue\n",
+ "\n",
+ " files = glob.glob(str(RUN_DIR / REGISTRY[model][\"family\"] / f\"{model}_*nvt.traj\"))\n",
+ "\n",
+ " for i, file in enumerate(files):\n",
+ " try:\n",
+ " traj = read(file, index=\":\")\n",
+ " except Exception as e:\n",
+ " print(f\"Error reading {file}: {e}\")\n",
+ " continue\n",
+ "\n",
+ " try:\n",
+ " stats = get_runtime_stats(traj, atoms0=traj[0])\n",
+ " except Exception as e:\n",
+ " print(f\"Error processing {file}: {e}\")\n",
+ " continue\n",
+ "\n",
+ " df = pd.concat(\n",
+ " [\n",
+ " df,\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " \"model\": model,\n",
+ " \"formula\": traj[0].get_chemical_formula(),\n",
+ " \"normalized_timestep\": stats[\"timestep\"]\n",
+ " / stats[\"target_steps\"],\n",
+ " \"normalized_final_step\": stats[\"final_step\"]\n",
+ " / stats[\"target_steps\"],\n",
+ " \"pressure\": np.array(stats[\"pressures\"]) / units.GPa,\n",
+ " }\n",
+ " | stats\n",
+ " ),\n",
+ " ],\n",
+ " ignore_index=True,\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "# import scipy.optimize as opt\n",
+ "import seaborn as sns\n",
+ "from scipy.optimize import curve_fit\n",
+ "\n",
+ "\n",
+ "# Define the power-law fitting function\n",
+ "def power_law(x, a, n):\n",
+ " return a * np.power(x, n)\n",
+ "\n",
+ "\n",
+ "df.rename(\n",
+ " columns={\n",
+ " \"final_step\": \"Total steps\",\n",
+ " \"model\": \"Model\",\n",
+ " },\n",
+ " inplace=True,\n",
+ ")\n",
+ "\n",
+ "with plt.style.context(\"default\"):\n",
+ " fig, axes = plt.subplot_mosaic(\n",
+ " \"\"\"\n",
+ " ao\n",
+ " \"\"\",\n",
+ " constrained_layout=True,\n",
+ " figsize=(6, 3),\n",
+ " width_ratios=[1, 3],\n",
+ " )\n",
+ "\n",
+ " iax = \"o\"\n",
+ " ax = axes.pop(iax)\n",
+ "\n",
+ " sns.scatterplot(\n",
+ " data=df,\n",
+ " x=\"natoms\",\n",
+ " y=\"steps_per_second\",\n",
+ " size=\"Total steps\",\n",
+ " hue=\"Model\",\n",
+ " ax=ax,\n",
+ " palette=method_color_mapping,\n",
+ " sizes=(1, 50),\n",
+ " # alpha=0.5\n",
+ " )\n",
+ "\n",
+ " # Fit and plot power-law regression for each model\n",
+ " for model, data in df.groupby(\"Model\"):\n",
+ " data.dropna(subset=[\"steps_per_second\"], inplace=True)\n",
+ "\n",
+ " popt, pcov = curve_fit(power_law, data[\"natoms\"], data[\"steps_per_second\"])\n",
+ "\n",
+ " # Generate smooth curve\n",
+ " # x_fit = np.logspace(np.log10(xdata.min()), np.log10(xdata.max()), 100)\n",
+ " # y_fit = power_law(x_fit, a_fit, n_fit)\n",
+ "\n",
+ " x = np.linspace(data[\"natoms\"].min(), data[\"natoms\"].max(), 100)\n",
+ "\n",
+ " # Plot regression line\n",
+ " ax.plot(\n",
+ " x,\n",
+ " power_law(x, *popt),\n",
+ " c=method_color_mapping[model],\n",
+ " # label=f\"{model} (y={a_fit:.2e}x^{n_fit:.2f})\",\n",
+ " linestyle=\"-\",\n",
+ " )\n",
+ "\n",
+ " # sns.lineplot(\n",
+ " # data=df,\n",
+ " # x='natoms',\n",
+ " # y='steps_per_second',\n",
+ " # # size='Total steps',\n",
+ " # hue='Model',\n",
+ " # ax=ax,\n",
+ " # palette=method_color_mapping,\n",
+ " # alpha=0.5,\n",
+ " # # err_style=\"bars\"\n",
+ " # )\n",
+ "\n",
+ " ax.set(\n",
+ " xlabel=\"Number of atoms\",\n",
+ " xscale=\"log\",\n",
+ " ylabel=\"Steps per second\",\n",
+ " yscale=\"log\",\n",
+ " )\n",
+ " ax.spines[\"right\"].set_visible(False)\n",
+ " ax.spines[\"top\"].set_visible(False)\n",
+ " ax.grid(alpha=0.25)\n",
+ " ax.legend(\n",
+ " loc=\"upper left\", bbox_to_anchor=(1.0, 1.0), fontsize=\"x-small\", frameon=False\n",
+ " )\n",
+ "\n",
+ " fisrt = 120\n",
+ "\n",
+ " for k, df_model in df.groupby(\"Model\"):\n",
+ " ax = axes[\"a\"]\n",
+ "\n",
+ " df_model.drop_duplicates([\"formula\"], inplace=True)\n",
+ " df_model = df_model[df_model[\"formula\"].isin(compositions[:fisrt])].copy()\n",
+ "\n",
+ " # Compute histogram\n",
+ " bins = np.linspace(0, 1, 50) # 50 bins from 0 to 1\n",
+ " hist, bin_edges = np.histogram(\n",
+ " df_model[\"normalized_final_step\"], bins=bins, density=False\n",
+ " )\n",
+ "\n",
+ " # Compute cumulative population\n",
+ " cumulative_population = np.cumsum(hist)\n",
+ "\n",
+ " # Midpoints for binning\n",
+ " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
+ "\n",
+ " sns.lineplot(\n",
+ " x=bin_centers[:-1],\n",
+ " y=(cumulative_population[-1] - cumulative_population[:-1]) / fisrt * 100,\n",
+ " ax=axes[\"a\"],\n",
+ " # label=k,\n",
+ " color=method_color_mapping[k],\n",
+ " # palette=method_color_mapping\n",
+ " )\n",
+ "\n",
+ " ax_main = axes[\"a\"]\n",
+ " ax_main.spines[\"right\"].set_visible(False)\n",
+ " ax_temp = ax_main.twiny()\n",
+ " # ax_pressure = ax_main.twiny()\n",
+ "\n",
+ " # === Plot styling and range ===\n",
+ " ax_main.set_xlim(0, 1)\n",
+ " # ax_main.set_ylim(0, 100)\n",
+ " # ax_main.set_yticks(range(0, 81, 20))\n",
+ " ax_main.set_ylabel(\"valid runs (%)\")\n",
+ "\n",
+ "\n",
+ " # === Set top x-axis: Time (ps) ===\n",
+ " ax_main.set_xticks([0, 1])\n",
+ " ax_main.set_xticklabels([0, 10])\n",
+ " ax_main.set_xlabel(\"Time (ps)\")\n",
+ " ax_main.xaxis.set_label_position(\"top\")\n",
+ " ax_main.xaxis.tick_top()\n",
+ " ax_main.spines[\"top\"].set_position((\"outward\", 5)) # Keep just below plot\n",
+ " # ax_main.tick_params(axis=\"x\", top=True, labeltop=True, bottom=False, labelbottom=False)\n",
+ "\n",
+ " # === Bottom axis: Temperature ===\n",
+ " ax_temp.set_xlim(ax_main.get_xlim())\n",
+ " ax_temp.set_xticks([0, 1])\n",
+ " ax_temp.set_xticklabels([\"300 K\", \"3000 K\"])\n",
+ " # ax_temp.set_xlabel(\"Temperature (K)\")\n",
+ " ax_temp.xaxis.set_ticks_position(\"bottom\")\n",
+ " ax_temp.xaxis.set_label_position(\"bottom\")\n",
+ " ax_temp.spines[\"right\"].set_visible(False)\n",
+ " ax_temp.spines[\"top\"].set_visible(False)\n",
+ " ax_temp.spines[\"bottom\"].set_position((\"outward\", 5)) # Keep just below plot\n",
+ "\n",
+ " # # === Clean up main axis ===\n",
+ " ax_main.legend_ = None\n",
+ "\n",
+ " plt.savefig(\"stability-and-speed-nvt-loglog.pdf\", bbox_inches=\"tight\")\n",
+ " plt.savefig(\n",
+ " \"stability-and-speed-nvt-loglog.png\", bbox_inches=\"tight\", dpi=330\n",
+ " )\n",
+ "\n",
+ " # plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
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diff --git a/benchmarks/stability/pressure.ipynb b/benchmarks/stability/pressure.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3d4714cfda9054c0c90f77a154938d9339e50d9b
--- /dev/null
+++ b/benchmarks/stability/pressure.ipynb
@@ -0,0 +1,194 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "from ase import units\n",
+ "from dask.distributed import Client\n",
+ "from dask_jobqueue import SLURMCluster\n",
+ "from dotenv import load_dotenv\n",
+ "from prefect import flow, task\n",
+ "from prefect_dask import DaskTaskRunner\n",
+ "\n",
+ "from mlip_arena.models import REGISTRY, MLIPEnum\n",
+ "from mlip_arena.tasks.md import run as MD\n",
+ "from mlip_arena.tasks.stability.input import get_atoms_from_db\n",
+ "\n",
+ "load_dotenv()\n",
+ "\n",
+ "HF_TOKEN = os.environ.get(\"HF_TOKEN\", None)\n",
+ "MP_API_KEY = os.environ.get(\"MP_API_KEY\", None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "nodes_per_alloc = 1\n",
+ "gpus_per_alloc = 4\n",
+ "ntasks = 1\n",
+ "\n",
+ "cluster_kwargs = dict(\n",
+ " cores=1,\n",
+ " memory=\"64 GB\",\n",
+ " processes=1,\n",
+ " shebang=\"#!/bin/bash\",\n",
+ " account=\"matgen\",\n",
+ " walltime=\"03:00:00\",\n",
+ " # job_cpu=128,\n",
+ " job_mem=\"0\",\n",
+ " job_script_prologue=[\n",
+ " \"source ~/.bashrc\",\n",
+ " \"module load python\",\n",
+ " \"source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\",\n",
+ " ],\n",
+ " job_directives_skip=[\"-n\", \"--cpus-per-task\", \"-J\"],\n",
+ " job_extra_directives=[\n",
+ " \"-J stability-npt\",\n",
+ " \"-q preempt\",\n",
+ " \"--time-min=00:30:00\",\n",
+ " \"--comment=12:00:00\",\n",
+ " f\"-N {nodes_per_alloc}\",\n",
+ " \"-C gpu\",\n",
+ " f\"-G {gpus_per_alloc}\",\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "cluster = SLURMCluster(**cluster_kwargs)\n",
+ "print(cluster.job_script())\n",
+ "cluster.adapt(minimum_jobs=5, maximum_jobs=10)\n",
+ "client = Client(cluster)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from mlip_arena.tasks.utils import get_calculator\n",
+ "\n",
+ "selected_models = [\n",
+ " \"MACE-MP(M)\",\n",
+ " \"CHGNet\",\n",
+ " \"M3GNet\",\n",
+ " \"MatterSim\",\n",
+ " \"eqV2(OMat)\",\n",
+ " \"MACE-MPA\",\n",
+ " \"ORBv2\",\n",
+ " \"SevenNet\",\n",
+ " \"ALIGNN\",\n",
+ "]\n",
+ "\n",
+ "\n",
+ "@task\n",
+ "def run_one(\n",
+ " atoms,\n",
+ " model,\n",
+ "):\n",
+ " result = MD.with_options(\n",
+ " timeout_seconds=600,\n",
+ " retries=2,\n",
+ " refresh_cache=True\n",
+ " )(\n",
+ " atoms=atoms,\n",
+ " calculator=get_calculator(\n",
+ " model.name,\n",
+ " calculator_kwargs=None,\n",
+ " ),\n",
+ " ensemble=\"npt\",\n",
+ " dynamics=\"nose-hoover\",\n",
+ " time_step=None,\n",
+ " dynamics_kwargs=dict(\n",
+ " ttime=25 * units.fs, pfactor=((75 * units.fs) ** 2) * 1e2 * units.GPa\n",
+ " ),\n",
+ " total_time=1e4, # 5e4, # fs\n",
+ " temperature=[300, 3000],\n",
+ " pressure=[0, 5e2 * units.GPa], # 500 GPa / 10 ps = 50 GPa / 1 ps\n",
+ " traj_file=f\"{REGISTRY[model.name]['family']}/{model.name}_{atoms.info.get('material_id', 'random')}_{atoms.get_chemical_formula()}_npt.traj\",\n",
+ " traj_interval=10,\n",
+ " )\n",
+ "\n",
+ " return result\n",
+ "\n",
+ "\n",
+ "@flow\n",
+ "def compress():\n",
+ " futures = []\n",
+ " # To download the database automatically, `huggingface_hub login` or provide HF_TOKEN\n",
+ " for atoms in get_atoms_from_db(\"random-mixture.db\", force_download=False):\n",
+ " for model in MLIPEnum:\n",
+ " if model.name not in selected_models:\n",
+ " continue\n",
+ "\n",
+ " if \"stability\" not in REGISTRY[model.name][\"gpu-tasks\"]:\n",
+ " continue\n",
+ "\n",
+ " try:\n",
+ " future = run_one.with_options(\n",
+ " timeout_seconds=600, retries=2, refresh_cache=False\n",
+ " ).submit(atoms.copy(), model)\n",
+ " futures.append(future)\n",
+ " except:\n",
+ " continue\n",
+ "\n",
+ " return [future.result(raise_on_failure=False) for future in futures]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "compress.with_options(\n",
+ " task_runner=DaskTaskRunner(address=client.scheduler.address), log_prints=True\n",
+ ")()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "NERSC Python",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "state": {},
+ "version_major": 2,
+ "version_minor": 0
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/benchmarks/stability/stability-and-speed-npt-loglog.pdf b/benchmarks/stability/stability-and-speed-npt-loglog.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..a9a50a1de1e7f6c639ca3ce348546e920f2d5ae0
--- /dev/null
+++ b/benchmarks/stability/stability-and-speed-npt-loglog.pdf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ddeedb81aa8828af91347e54a63ea0c41b5947a429e3fe9cd79892889d77a94
+size 516662
diff --git a/benchmarks/stability/stability-and-speed-nvt-loglog.pdf b/benchmarks/stability/stability-and-speed-nvt-loglog.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..031c02165f72d47f9b86bc2246b3d6d454dab60d
--- /dev/null
+++ b/benchmarks/stability/stability-and-speed-nvt-loglog.pdf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c27340ca8523bd4766561e00361cffaf6810baedd492b6f7a199832d42a70c65
+size 1247826
diff --git a/benchmarks/stability/temperature.ipynb b/benchmarks/stability/temperature.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..456a661d9bc375ca42c1a14f510401d1be7520db
--- /dev/null
+++ b/benchmarks/stability/temperature.ipynb
@@ -0,0 +1,202 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "from ase import units\n",
+ "from dask.distributed import Client\n",
+ "from dask_jobqueue import SLURMCluster\n",
+ "from dotenv import load_dotenv\n",
+ "from prefect import flow, task\n",
+ "from prefect_dask import DaskTaskRunner\n",
+ "\n",
+ "from mlip_arena.models import REGISTRY, MLIPEnum\n",
+ "from mlip_arena.tasks.md import run as MD\n",
+ "from mlip_arena.tasks.stability.input import get_atoms_from_db\n",
+ "\n",
+ "load_dotenv()\n",
+ "\n",
+ "HF_TOKEN = os.environ.get(\"HF_TOKEN\", None)\n",
+ "MP_API_KEY = os.environ.get(\"MP_API_KEY\", None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "nodes_per_alloc = 1\n",
+ "gpus_per_alloc = 4\n",
+ "ntasks = 1\n",
+ "\n",
+ "cluster_kwargs = dict(\n",
+ " cores=1,\n",
+ " memory=\"64 GB\",\n",
+ " processes=1,\n",
+ " shebang=\"#!/bin/bash\",\n",
+ " account=\"matgen\",\n",
+ " walltime=\"04:00:00\",\n",
+ " # job_cpu=128,\n",
+ " job_mem=\"0\",\n",
+ " job_script_prologue=[\n",
+ " \"source ~/.bashrc\",\n",
+ " \"module load python\",\n",
+ " \"source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\",\n",
+ " ],\n",
+ " job_directives_skip=[\"-n\", \"--cpus-per-task\", \"-J\"],\n",
+ " job_extra_directives=[\n",
+ " \"-J stability-nvt\",\n",
+ " \"-q preempt\",\n",
+ " \"--time-min=00:30:00\",\n",
+ " \"--comment=12:00:00\",\n",
+ " f\"-N {nodes_per_alloc}\",\n",
+ " \"-C gpu\",\n",
+ " f\"-G {gpus_per_alloc}\",\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "cluster = SLURMCluster(**cluster_kwargs)\n",
+ "print(cluster.job_script())\n",
+ "cluster.adapt(minimum_jobs=10, maximum_jobs=50)\n",
+ "client = Client(cluster)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from prefect.cache_policies import INPUTS, TASK_SOURCE\n",
+ "from prefect.futures import wait\n",
+ "\n",
+ "from mlip_arena.tasks.utils import get_calculator\n",
+ "\n",
+ "selected_models = [\n",
+ " \"MACE-MP(M)\",\n",
+ " \"CHGNet\",\n",
+ " \"M3GNet\",\n",
+ " \"MatterSim\",\n",
+ " \"eqV2(OMat)\",\n",
+ " \"MACE-MPA\",\n",
+ " \"ORBv2\",\n",
+ " \"SevenNet\",\n",
+ " \"ALIGNN\",\n",
+ "]\n",
+ "\n",
+ "\n",
+ "@task(cache_policy=TASK_SOURCE + INPUTS)\n",
+ "def run_one(\n",
+ " atoms,\n",
+ " model,\n",
+ "):\n",
+ " try:\n",
+ " result = MD.with_options(\n",
+ " # timeout_seconds=600,\n",
+ " # retries=1,\n",
+ " refresh_cache=True\n",
+ " )(\n",
+ " atoms=atoms,\n",
+ " calculator=get_calculator(\n",
+ " model.name,\n",
+ " calculator_kwargs=None,\n",
+ " ),\n",
+ " ensemble=\"nvt\",\n",
+ " dynamics=\"nose-hoover\",\n",
+ " time_step=None,\n",
+ " dynamics_kwargs=dict(\n",
+ " ttime=25 * units.fs,\n",
+ " # pfactor=((75 * units.fs) ** 2) * 1e2 * units.GPa\n",
+ " ),\n",
+ " total_time=1e4, # 5e4, # fs\n",
+ " temperature=[300, 3000],\n",
+ " pressure=None,\n",
+ " traj_file=f\"{REGISTRY[model.name]['family']}/{model.name}_{atoms.info.get('material_id', 'random')}_{atoms.get_chemical_formula()}_nvt.traj\",\n",
+ " traj_interval=10,\n",
+ " )\n",
+ " except Exception as e:\n",
+ " print(e)\n",
+ " return e\n",
+ "\n",
+ " return result\n",
+ "\n",
+ "\n",
+ "@flow\n",
+ "def heat():\n",
+ " futures = []\n",
+ " # To download the database automatically, `huggingface_hub login` or provide HF_TOKEN\n",
+ " for atoms in get_atoms_from_db(\"random-mixture.db\", force_download=False):\n",
+ " for model in MLIPEnum:\n",
+ " if model.name not in selected_models:\n",
+ " continue\n",
+ "\n",
+ " future = run_one.with_options(\n",
+ " timeout_seconds=600, retries=2, refresh_cache=False\n",
+ " ).submit(atoms.copy(), model)\n",
+ " futures.append(future)\n",
+ "\n",
+ " wait(futures)\n",
+ "\n",
+ " return [\n",
+ " f.result(timeout=None, raise_on_failure=False)\n",
+ " for f in futures\n",
+ " if f.state.is_completed()\n",
+ " ]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "heat.with_options(\n",
+ " task_runner=DaskTaskRunner(address=client.scheduler.address), log_prints=True\n",
+ ")()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "mlip-arena",
+ "language": "python",
+ "name": "mlip-arena"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "state": {},
+ "version_major": 2,
+ "version_minor": 0
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/benchmarks/vacancy_migration/Table-A1-fcc.csv b/benchmarks/vacancy_migration/Table-A1-fcc.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a08d6b18ebaaa66c64a012158df33f3498e91133
--- /dev/null
+++ b/benchmarks/vacancy_migration/Table-A1-fcc.csv
@@ -0,0 +1,58 @@
+symbol,cohesive_energy,K,K',volume_per_atom,e_vacform,e_vacmig
+Ac,3.70,21.63,2.03,45.37,1.26,0.45
+Ag,2.49,84.11,5.15,17.87,0.68,0.70
+Al,3.43,76.90,4.23,16.47,0.61,0.58
+Ar,0.02,1.60,3.45,45.00,0.01,0.06
+Au,2.99,136.19,7.45,18.07,0.40,0.53
+Ba,1.87,8.01,2.81,64.13,1.09,0.36
+Be,3.64,116.96,3.90,7.88,-0.06,0.75
+Ca,1.91,16.82,1.70,42.17,1.13,0.47
+Cd,0.74,41.66,5.71,22.60,0.30,0.23
+Ce,4.58,37.65,4.10,26.10,1.30,0.54
+Co,4.92,251.54,5.17,10.30,1.84,1.45
+Co_mag,5.11,209.89,5.07,10.90,1.79,1.01
+Cs,0.70,2.40,2.36,116.46,0.34,0.13
+Cu,3.48,136.99,5.86,11.97,1.07,0.72
+Dy,4.23,,,31.37,1.72,
+Er,1.55,16.02,2.66,40.96,1.02,0.47
+Fe,4.69,283.59,4.89,10.22,2.32,1.38
+Ga,2.61,29.64,6.02,18.91,-0.04,0.18
+Ge,3.40,,,19.51,0.10,
+He,0.01,1.60,3.36,16.54,0.00,0.02
+Hf,6.41,107.35,3.59,22.26,2.09,0.81
+Ho,4.20,40.86,4.04,30.88,1.74,0.73
+In,2.31,33.65,4.53,27.33,0.28,0.23
+Ir,7.23,345.27,5.44,14.53,1.55,2.54
+K,0.86,3.20,6.19,73.60,0.34,0.16
+Kr,0.02,0.80,7.90,57.44,0.00,0.07
+La,4.22,24.83,3.36,37.10,1.44,0.21
+Li,1.61,13.62,3.75,20.22,0.60,0.13
+Mg,1.49,36.05,4.04,23.03,0.82,0.41
+Mn,3.76,276.38,5.42,10.73,2.38,0.65
+Mn_mag,3.76,275.57,5.58,10.72,2.36,0.69
+Na,1.11,9.61,3.08,35.08,0.38,0.14
+Ni,4.77,197.87,5.29,10.85,1.39,0.94
+Ni_mag,4.83,193.06,5.45,10.90,1.43,1.08
+Os,8.17,398.14,4.99,14.29,2.75,2.73
+Pa,6.86,96.13,4.16,25.21,1.54,0.77
+Pb,2.94,39.25,4.07,31.69,0.45,0.54
+Pd,3.71,165.83,6.62,15.37,1.16,0.95
+Pr,3.58,,,23.79,0.76,
+Pt,5.45,246.74,6.10,15.67,0.61,1.24
+Rb,0.76,2.40,7.13,90.81,0.30,0.14
+Re,7.73,366.10,4.73,14.94,2.94,1.81
+Rh,5.63,250.74,5.86,14.13,1.57,1.79
+Ru,6.56,314.03,4.81,13.88,2.51,1.85
+Sc,4.09,54.47,4.07,24.43,2.07,0.60
+Sn,3.11,46.46,6.10,27.82,0.26,0.38
+Sr,1.61,11.22,3.72,54.74,0.95,0.45
+Ta,8.09,198.67,3.86,18.63,2.23,
+Tb,4.26,38.45,4.55,31.88,1.75,0.71
+Tc,6.84,298.81,4.91,14.50,2.62,1.17
+Th,6.34,55.28,3.50,32.07,1.92,1.25
+Ti,5.43,102.54,2.89,17.25,1.95,0.45
+Tl,1.99,27.24,5.55,30.50,0.37,0.10
+W,7.98,,,16.25,1.67,
+Xe,0.03,0.80,2.22,80.21,0.01,0.09
+Y,4.14,38.45,4.10,32.35,1.72,0.67
+Zr,6.21,90.52,4.42,23.26,2.02,0.50
\ No newline at end of file
diff --git a/benchmarks/vacancy_migration/Table-A2-hcp.csv b/benchmarks/vacancy_migration/Table-A2-hcp.csv
new file mode 100644
index 0000000000000000000000000000000000000000..408dd62e938b50f68ac7d735f7684d3d73d1cddd
--- /dev/null
+++ b/benchmarks/vacancy_migration/Table-A2-hcp.csv
@@ -0,0 +1,58 @@
+symbol,cohesive_energy,K,K',volume_per_atom,e_vacform,e_vacmig_v,e_vacmig
+Ag,2.49,82.51,5.15,17.93,0.76,0.50,0.60
+Al,3.40,74.50,4.70,16.63,0.62,0.41,0.46
+Ar,0.02,0.88,7.43,46.11,0.01,0.06,
+Au,2.98,130.58,7.58,18.10,0.40,0.49,0.57
+Ba,1.87,8.01,3.38,63.55,1.11,0.34,0.37
+Be,3.72,122.57,3.57,7.92,1.04,0.74,0.87
+Bi,2.38,,,31.44,0.00,,
+Ca,1.91,17.62,3.44,42.39,1.06,0.38,0.42
+Cd,0.74,43.26,6.04,22.60,0.25,0.23,0.16
+Ce,4.49,,,26.43,1.23,,
+Co,4.90,248.34,3.29,10.33,1.67,1.02,1.19
+Co_mag,5.12,214.69,4.91,10.85,1.93,0.81,0.78
+Cr,3.63,,,11.77,1.77,,
+Cs,0.70,1.60,7.60,116.42,0.31,0.12,0.13
+Cu,3.47,134.58,5.61,12.00,1.04,0.57,0.69
+Fe,4.77,288.39,5.60,10.16,2.43,1.52,1.51
+Ga,2.60,44.06,4.37,18.92,0.21,0.12,0.16
+Ge,3.40,,,19.25,0.26,,
+He,0.01,1.60,3.46,16.54,0.00,0.02,0.01
+Hf,6.48,112.95,3.59,22.26,2.26,0.89,1.00
+In,2.31,32.84,4.68,27.40,0.31,0.20,0.21
+Ir,7.16,339.66,3.32,14.60,1.25,1.57,1.95
+K,0.86,3.20,5.95,73.72,0.35,0.12,0.14
+Kr,0.02,0.80,6.73,58.10,0.00,0.07,0.07
+La,4.20,,,37.42,1.45,,
+Li,1.61,13.62,3.82,20.24,0.63,0.10,0.12
+Mg,1.50,36.05,4.31,22.85,0.78,0.40,0.42
+Mn,3.82,190.66,11.04,10.63,2.51,0.43,0.11
+Mo,5.91,,,15.95,1.96,,
+Nb,6.71,,,18.61,2.08,,
+Ne,0.01,6.41,1.65,19.15,-0.01,0.04,0.04
+Ni,4.74,134.58,12.07,10.87,1.35,0.69,0.79
+Ni_mag,4.81,192.26,5.34,10.94,1.37,0.77,0.89
+Os,8.31,406.15,5.04,14.23,3.03,3.03,3.22
+Pa,3.09,90.52,3.96,15.12,0.28,0.38,
+Pb,2.92,37.65,4.49,31.51,0.41,0.44,0.41
+Pd,3.67,163.42,6.50,15.45,1.10,0.68,0.73
+Pt,5.39,241.13,6.22,15.77,0.70,0.70,
+Rb,0.76,2.40,6.45,91.19,0.32,0.13,0.12
+Re,7.79,370.10,4.67,14.89,3.42,1.79,1.17
+Rh,5.59,250.74,5.76,14.18,1.53,1.25,1.47
+Ru,6.68,311.62,5.16,13.81,2.68,2.17,2.18
+Sc,4.14,52.87,4.82,24.48,1.87,0.74,0.69
+Si,4.04,85.72,4.88,14.35,0.08,0.04,0.26
+Sn,3.11,47.26,5.92,27.55,0.40,0.31,0.33
+Sr,1.61,11.22,3.77,55.16,0.98,0.36,0.40
+Ta,8.05,,,18.55,2.35,,
+Tc,6.91,302.01,4.90,14.44,2.85,1.21,0.63
+Te,2.23,48.87,5.03,31.47,0.25,,
+Ti,5.49,109.75,5.07,17.29,2.06,0.49,0.59
+Tl,2.01,25.63,5.47,30.97,0.40,0.23,0.16
+V,5.15,,,13.75,1.84,,
+W,7.96,,,16.32,2.44,,
+Xe,0.03,0.80,2.32,79.60,0.01,0.07,0.08
+Y,4.16,40.05,3.72,32.82,1.87,0.69,0.67
+Zn,1.10,73.70,6.02,15.23,0.47,0.34,0.20
+Zr,6.25,93.73,4.34,23.44,2.03,0.57,0.70
\ No newline at end of file
diff --git a/benchmarks/vacancy_migration/analysis.py b/benchmarks/vacancy_migration/analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3c250d139d71c9c48d03116e1457de1d7b6256b
--- /dev/null
+++ b/benchmarks/vacancy_migration/analysis.py
@@ -0,0 +1,284 @@
+import glob
+import pickle
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+from pymatgen.core import Element
+
+from mlip_arena.models import REGISTRY
+
+DATA_DIR = Path(__file__).parent
+
+mlip_models = ["MACE-MP(M)", "MatterSim", "ORBv2", "M3GNet", "CHGNet", "SevenNet"]
+
+fcc_pbe = pd.read_csv(DATA_DIR / "Table-A1-fcc.csv")
+hcp_pbe = pd.read_csv(DATA_DIR / "Table-A2-hcp.csv")
+
+# fcc
+
+# Initialize an empty DataFrame
+results_df = pd.DataFrame(columns=["symbol", "model", "fit_path", "fit_energies"])
+
+for model in mlip_models:
+ out_dir = Path(REGISTRY[model]["family"])
+
+ for index, row in fcc_pbe.iterrows():
+ symbol = row["symbol"]
+
+ if Element(symbol.split("_")[0]).is_noble_gas:
+ continue
+
+ files = glob.glob(str(out_dir / f"{model}-fcc-{symbol.split('_')[0]}108.pkl"))
+ if len(files) == 0:
+ print("skip", model, symbol)
+ # Add missing data to the DataFrame
+ # if symbol not in results_df['symbol'].values:
+ # Create a new row if the symbol is not yet in the DataFrame
+ new_row = {
+ "symbol": symbol,
+ "model": model,
+ "pbe_e_vacmig": row["e_vacmig"],
+ "fit_path": [],
+ "fit_energies": [],
+ }
+ results_df = pd.concat(
+ [results_df, pd.DataFrame([new_row])], ignore_index=True
+ )
+ continue
+ file = files[0]
+ with open(file, "rb") as f:
+ result = pickle.load(f)
+
+ # Add data to the DataFrame
+ # if symbol not in results_df['symbol'].values:
+ # Create a new row if the symbol is not yet in the DataFrame
+ forcefit = result["neb"]["forcefit"]
+ new_row = {
+ "symbol": symbol,
+ "model": model,
+ "pbe_e_vacmig": row["e_vacmig"],
+ "fit_path": forcefit.fit_path,
+ "fit_energies": forcefit.fit_energies,
+ }
+ results_df = pd.concat([results_df, pd.DataFrame([new_row])], ignore_index=True)
+
+
+nrows = 2
+ncols = len(mlip_models) // nrows
+
+fig, axes = plt.subplots(
+ nrows=nrows,
+ ncols=ncols,
+ figsize=(6, 4),
+ sharex=True,
+ sharey=True,
+ constrained_layout=True,
+ dpi=300,
+)
+
+for i, (ax, model) in enumerate(zip(axes.ravel(), mlip_models, strict=False)):
+ filtered_df = results_df[results_df["model"] == model]
+
+ asymmetries = []
+ middle_deviations = []
+
+ for index, row in filtered_df.iterrows():
+ if len(row["fit_path"]) == 0 or pd.isna(row["pbe_e_vacmig"]):
+ continue
+
+ x = row["fit_path"] / max(row["fit_path"])
+ y = row["fit_energies"] / row["pbe_e_vacmig"]
+
+ # middle_idx = np.argmin(np.abs(x - 0.5))
+
+ left_side = y[x <= 0.5]
+ right_side = y[x >= 0.5][::-1]
+ min_len = min(len(left_side), len(right_side))
+ left_side = left_side[:min_len]
+ right_side = right_side[:min_len]
+
+ asymmetry = np.abs(left_side - right_side).mean()
+ # middle = (left_side[-1] + right_side[-1]) / 2
+ middle = max(y)
+
+ if np.abs(np.array(y)).max() > 10:
+ continue
+
+ asymmetries.append(asymmetry)
+ middle_deviations.append(middle - 1)
+
+ ax.plot(
+ x,
+ y,
+ alpha=0.5,
+ color=method_color_mapping[model],
+ label=model,
+ )
+
+ asymmetries = np.array(asymmetries)
+ middle_deviations = np.array(middle_deviations)
+
+ ax.text(
+ 0.05,
+ 0.95,
+ "\n".join(
+ [
+ f"Miss: {len(filtered_df) - len(asymmetries) - filtered_df['pbe_e_vacmig'].isna().sum()}",
+ f"Asym: {asymmetries.mean():.3f}",
+ f"MAPE@max: {np.abs(middle_deviations).mean() * 100:.1f}",
+ ]
+ ),
+ transform=ax.transAxes,
+ ha="left",
+ va="top",
+ fontsize="small",
+ # fontsize=6,
+ )
+
+ ax.set(
+ title=model,
+ xlabel="Normalized path" if i >= len(models) - ncols else None,
+ ylabel="Normalized energy" if i % ncols == 0 else None,
+ ylim=(-0.1, 2),
+ )
+
+with open(DATA_DIR / "fcc.pkl", "wb") as f:
+ pickle.dump(fig, f)
+
+# hcp
+
+# Initialize an empty DataFrame
+results_df = pd.DataFrame(columns=["symbol", "model", "fit_path", "fit_energies"])
+
+for model in mlip_models:
+ out_dir = Path(REGISTRY[model]["family"])
+
+ for index, row in hcp_pbe.iterrows():
+ symbol = row["symbol"]
+
+ if Element(symbol.split("_")[0]).is_noble_gas:
+ continue
+
+ files = glob.glob(str(out_dir / f"{model}-hcp-{symbol.split('_')[0]}36.pkl"))
+ if len(files) == 0:
+ print("skip", model, symbol)
+ # Add missing data to the DataFrame
+ # if symbol not in results_df['symbol'].values:
+ # Create a new row if the symbol is not yet in the DataFrame
+ new_row = {
+ "symbol": symbol,
+ "model": model,
+ "pbe_e_vacmig": row["e_vacmig"],
+ "fit_path": [],
+ "fit_energies": [],
+ }
+ results_df = pd.concat(
+ [results_df, pd.DataFrame([new_row])], ignore_index=True
+ )
+ # else:
+ # # Update the existing row with the model's prediction
+ # results_df.loc[results_df['symbol'] == symbol, model] = pd.NA
+ continue
+ file = files[0]
+ with open(file, "rb") as f:
+ result = pickle.load(f)
+
+ # Add data to the DataFrame
+ # if symbol not in results_df['symbol'].values:
+ # Create a new row if the symbol is not yet in the DataFrame
+ forcefit = result["neb"]["forcefit"]
+ new_row = {
+ "symbol": symbol,
+ "model": model,
+ "pbe_e_vacmig": row["e_vacmig"],
+ "fit_path": forcefit.fit_path,
+ "fit_energies": forcefit.fit_energies,
+ }
+ results_df = pd.concat([results_df, pd.DataFrame([new_row])], ignore_index=True)
+
+
+
+nrows = 2
+ncols = len(mlip_models) // nrows
+
+threshold = 0.10
+
+fig, axes = plt.subplots(
+ nrows=nrows,
+ ncols=ncols,
+ figsize=(6, 4),
+ sharex=True,
+ sharey=True,
+ constrained_layout=True,
+ dpi=300,
+)
+
+for i, (ax, model) in enumerate(zip(axes.ravel(), mlip_models, strict=False)):
+ filtered_df = results_df[results_df["model"] == model]
+
+ asymmetries = []
+ middle_deviations = []
+
+ for index, row in filtered_df.iterrows():
+ if len(row["fit_path"]) == 0 or pd.isna(row["pbe_e_vacmig"]):
+ continue
+
+ x = row["fit_path"] / max(row["fit_path"])
+ y = row["fit_energies"] / row["pbe_e_vacmig"]
+
+ # middle_idx = np.argmin(np.abs(x - 0.5))
+
+ left_side = y[x <= 0.5]
+ right_side = y[x >= 0.5][::-1]
+ min_len = min(len(left_side), len(right_side))
+ left_side = left_side[:min_len]
+ right_side = right_side[:min_len]
+
+ asymmetry = np.abs(left_side - right_side).mean()
+ # middle = (left_side[-1] + right_side[-1]) / 2
+ middle = max(y)
+
+ if np.abs(np.array(y)).max() > 10:
+ continue
+
+ asymmetries.append(asymmetry)
+ middle_deviations.append(middle - 1)
+
+ ax.plot(
+ x,
+ y,
+ alpha=0.5,
+ color=method_color_mapping[model],
+ label=model,
+ )
+
+ asymmetries = np.array(asymmetries)
+ middle_deviations = np.array(middle_deviations)
+
+ ax.text(
+ 0.05,
+ 0.95,
+ "\n".join(
+ [
+ f"Miss: {len(filtered_df) - len(asymmetries) - filtered_df['pbe_e_vacmig'].isna().sum()}",
+ f"Asym: {asymmetries.mean():.3f}",
+ f"MAPE@max: {np.abs(middle_deviations).mean() * 100:.1f}",
+ ]
+ ),
+ transform=ax.transAxes,
+ ha="left",
+ va="top",
+ fontsize="small",
+ )
+
+ ax.set(
+ title=model,
+ xlabel="Normalized path" if i >= len(mlip_models) - ncols else None,
+ ylabel="Normalized energy" if i % ncols == 0 else None,
+ ylim=(-0.1, 2),
+ )
+
+with open(DATA_DIR / "hcp.pkl", "wb") as f:
+ pickle.dump(fig, f)
diff --git a/benchmarks/vacancy_migration/run.py b/benchmarks/vacancy_migration/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..4678d4d4392b25c24094f078e40449402952c3ee
--- /dev/null
+++ b/benchmarks/vacancy_migration/run.py
@@ -0,0 +1,167 @@
+import pickle
+from functools import partial
+from pathlib import Path
+
+from ase import Atoms
+from prefect import Task, flow, task
+from prefect.client.schemas.objects import TaskRun
+from prefect.results import ResultRecord
+from prefect.states import State
+
+from mlip_arena.models import REGISTRY, MLIPEnum
+from mlip_arena.tasks.eos import run as EOS
+from mlip_arena.tasks.neb import run_from_endpoints as NEB
+from mlip_arena.tasks.vacancy_migration.input import get_fcc_pristine, get_hcp_pristine
+
+MP_API_KEY = None
+
+test_models = ["MACE-MP(M)", "MatterSim", "ORBv2", "CHGNet", "M3GNet", "SevenNet"]
+
+
+def save_to_pickle(
+ tsk: Task, run: TaskRun, state: State, crystal: str
+):
+ result = run.state.result(raise_on_failure=False)
+
+ pristine = result["pristine"]
+ calculator_name = result["calculator_name"]
+ calculator_name = calculator_name.name if isinstance(calculator_name, MLIPEnum) else calculator_name
+
+ family_path = Path(REGISTRY[calculator_name]["family"])
+ family_path.mkdir(parents=True, exist_ok=True)
+
+ with open(family_path / f"{calculator_name}-{crystal}-{pristine.get_chemical_formula()}.pkl", "wb") as f:
+ pickle.dump(result, f)
+
+ # with open(family_path / f"{crystal}-{pristine.get_chemical_formula()}.json", 'w') as f:
+ # json.dump(result, f)
+
+@task
+def calculate_vacancy_migration(
+ pristine: Atoms,
+ istart: int,
+ iend: int,
+ calculator_name: MLIPEnum | str,
+ optimizer: str,
+ criterion: dict = {}
+):
+
+ eos = EOS.with_options(refresh_cache=True, persist_result=True)(
+ atoms=pristine,
+ calculator_name=calculator_name,
+ optimizer=optimizer,
+ criterion=criterion,
+ concurrent=False,
+ )
+
+ if isinstance(eos, ResultRecord):
+ eos = eos.result
+
+ if isinstance(eos, dict):
+ pristine = eos["atoms"]
+ else:
+ return eos
+
+ atoms = pristine.copy()
+ del atoms[istart]
+ start = atoms.copy()
+
+ atoms = pristine.copy()
+ del atoms[iend]
+ end = atoms.copy()
+
+
+ neb = NEB.with_options(refresh_cache=True, persist_result=True)(
+ start, end, n_images=7,
+ calculator_name=calculator_name,
+ optimizer=optimizer,
+ criterion=criterion,
+ relax_end_points=True
+ )
+
+ e_defect = 0.5 * (neb["images"][0].get_potential_energy() + neb["images"][-1].get_potential_energy())
+ e_pristine = pristine.get_potential_energy()
+
+ e_vacform = e_defect - (len(neb["images"][0]) / len(pristine)) * e_pristine
+
+ e_vacmig = neb["barrier"][0]
+ asymmetry = abs(neb["barrier"][1] / e_vacmig)
+
+ # TODO: temporary solution to pickling problem of mattersim
+ pristine.calc = None
+ for image in neb["images"]:
+ image.calc = None
+ eos["atoms"].calc = None
+
+ return {
+ "pristine": pristine,
+ "calculator_name": calculator_name,
+ "e_vacform": e_vacform,
+ "e_vacmig": e_vacmig,
+ "asymmetry": asymmetry,
+ "neb": neb,
+ "eos": eos
+ }
+
+@flow(persist_result=True, result_serializer="pickle")
+def run_fcc():
+
+ futures = []
+ for atoms in get_fcc_pristine(MP_API_KEY):
+ for model in MLIPEnum:
+ if model.name not in test_models:
+ continue
+ try:
+ result = calculate_vacancy_migration.with_options(
+ refresh_cache=True, persist_result=True,
+ on_completion=[partial(save_to_pickle, crystal="fcc")]
+ )(
+ pristine=atoms,
+ istart=0,
+ iend=1,
+ calculator_name=model,
+ optimizer="BFGS",
+ criterion=dict(fmax=0.05, steps=500),
+ )
+ except Exception:
+ continue
+ futures.append(result)
+
+ return futures
+ # wait(futures)
+ # return [f.result(raise_on_failure=False) for f in futures if f.state.is_completed()]
+
+@flow(persist_result=True, result_serializer="pickle")
+def run_hcp():
+ futures = []
+ for i, atoms in enumerate(get_hcp_pristine(MP_API_KEY)):
+ if i <= 30:
+ continue
+ for model in MLIPEnum:
+ if model.name not in test_models:
+ continue
+ try:
+ result = calculate_vacancy_migration.with_options(
+ refresh_cache=True, persist_result=True,
+ on_completion=[partial(save_to_pickle, crystal="hcp")]
+ )(
+ pristine=atoms,
+ istart=0,
+ iend=1,
+ calculator_name=model,
+ optimizer="BFGS",
+ criterion=dict(fmax=0.05, steps=500),
+ )
+ # calculator_name = model.name if isinstance(model, MLIPEnum) else model
+
+ # family_path = Path(REGISTRY[calculator_name]['family'])
+ # family_path.mkdir(parents=True, exist_ok=True)
+ # with open(family_path / f"{'hcp'}-{atoms.get_chemical_formula()}.pkl", 'wb') as f:
+ # pickle.dump(result, f)
+ except Exception:
+ continue
+ futures.append(result)
+
+ return futures
+ # wait(futures)
+ # return [f.result(raise_on_failure=False) for f in futures if f.state.is_completed()]
diff --git a/benchmarks/wbm_ev/ALIGNN.parquet b/benchmarks/wbm_ev/ALIGNN.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..34cbb20eed3a2d47dd00221e4490019419962768
--- /dev/null
+++ b/benchmarks/wbm_ev/ALIGNN.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b84592b56c667f49e510f382c07f2dd4105df71468c2198c3958b2d0066202b
+size 425244
diff --git a/benchmarks/wbm_ev/ALIGNN_processed.parquet b/benchmarks/wbm_ev/ALIGNN_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..869ce9da430e8bae54d8f87774b5ec9a65cef4e2
--- /dev/null
+++ b/benchmarks/wbm_ev/ALIGNN_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 368670
diff --git a/benchmarks/wbm_ev/CHGNet.parquet b/benchmarks/wbm_ev/CHGNet.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..9437015c0dc08d2b508481c8ec170a6759dc9f1c
--- /dev/null
+++ b/benchmarks/wbm_ev/CHGNet.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 424462
diff --git a/benchmarks/wbm_ev/CHGNet_processed.parquet b/benchmarks/wbm_ev/CHGNet_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..80b7548f9916569072be59ce659435b39bb1592e
--- /dev/null
+++ b/benchmarks/wbm_ev/CHGNet_processed.parquet
@@ -0,0 +1,3 @@
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+oid sha256:62fc23bf5bc94ba30c32581a8c57131a9125993a3f0e380cb3800220b197b666
+size 357806
diff --git a/benchmarks/wbm_ev/M3GNet.parquet b/benchmarks/wbm_ev/M3GNet.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..27f064dc4d9be72439dc73e462c7a7d56f59d6f1
--- /dev/null
+++ b/benchmarks/wbm_ev/M3GNet.parquet
@@ -0,0 +1,3 @@
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+size 411741
diff --git a/benchmarks/wbm_ev/M3GNet_processed.parquet b/benchmarks/wbm_ev/M3GNet_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..39037579f59af990128b9f909b60a99ad45fef8f
--- /dev/null
+++ b/benchmarks/wbm_ev/M3GNet_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd34bfe4650c2be26e7cfd75d747885ca265cbbc7fb769222d1f7e304fbd6de3
+size 357909
diff --git a/benchmarks/wbm_ev/MACE-MP(M).parquet b/benchmarks/wbm_ev/MACE-MP(M).parquet
new file mode 100644
index 0000000000000000000000000000000000000000..64273d7e076572a111355f3b18b5a0b28486bcb4
--- /dev/null
+++ b/benchmarks/wbm_ev/MACE-MP(M).parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1551823daef888914de951f2610ee6ffdfd2b0d6f33e1e293614534cdd217196
+size 409083
diff --git a/benchmarks/wbm_ev/MACE-MP(M)_processed.parquet b/benchmarks/wbm_ev/MACE-MP(M)_processed.parquet
new file mode 100644
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--- /dev/null
+++ b/benchmarks/wbm_ev/MACE-MP(M)_processed.parquet
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+version https://git-lfs.github.com/spec/v1
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+size 359889
diff --git a/benchmarks/wbm_ev/MACE-MPA.parquet b/benchmarks/wbm_ev/MACE-MPA.parquet
new file mode 100644
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--- /dev/null
+++ b/benchmarks/wbm_ev/MACE-MPA.parquet
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diff --git a/benchmarks/wbm_ev/MACE-MPA_processed.parquet b/benchmarks/wbm_ev/MACE-MPA_processed.parquet
new file mode 100644
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--- /dev/null
+++ b/benchmarks/wbm_ev/MACE-MPA_processed.parquet
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+version https://git-lfs.github.com/spec/v1
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diff --git a/benchmarks/wbm_ev/MatterSim.parquet b/benchmarks/wbm_ev/MatterSim.parquet
new file mode 100644
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--- /dev/null
+++ b/benchmarks/wbm_ev/MatterSim.parquet
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+version https://git-lfs.github.com/spec/v1
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+size 408998
diff --git a/benchmarks/wbm_ev/MatterSim_processed.parquet b/benchmarks/wbm_ev/MatterSim_processed.parquet
new file mode 100644
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--- /dev/null
+++ b/benchmarks/wbm_ev/MatterSim_processed.parquet
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+version https://git-lfs.github.com/spec/v1
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diff --git a/benchmarks/wbm_ev/ORBv2.parquet b/benchmarks/wbm_ev/ORBv2.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..a7c753891453cb6a880ebce6d267c0d2d5c14984
--- /dev/null
+++ b/benchmarks/wbm_ev/ORBv2.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 415496
diff --git a/benchmarks/wbm_ev/ORBv2_processed.parquet b/benchmarks/wbm_ev/ORBv2_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..42735cc0523daf4b1f354ae6014f81dc70cf6115
--- /dev/null
+++ b/benchmarks/wbm_ev/ORBv2_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 358072
diff --git a/benchmarks/wbm_ev/SevenNet.parquet b/benchmarks/wbm_ev/SevenNet.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..b053a0e5f2cc8d69480b94d6a47762f252029f2e
--- /dev/null
+++ b/benchmarks/wbm_ev/SevenNet.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6850c333c0b754b942efcfac11739ae199a9f3c816da4e0e6b26bc9a037a0524
+size 410197
diff --git a/benchmarks/wbm_ev/SevenNet_processed.parquet b/benchmarks/wbm_ev/SevenNet_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..b1b812b5f2315d85fe3e606555a15a320468e966
--- /dev/null
+++ b/benchmarks/wbm_ev/SevenNet_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 358468
diff --git a/benchmarks/wbm_ev/analyze.py b/benchmarks/wbm_ev/analyze.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebc5bef0ea0b85eb3a74dbfefe90e7058ef6231d
--- /dev/null
+++ b/benchmarks/wbm_ev/analyze.py
@@ -0,0 +1,210 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from ase.db import connect
+from scipy import stats
+
+from mlip_arena.models import REGISTRY, MLIPEnum
+
+DATA_DIR = Path(__file__).parent.absolute()
+
+
+def load_wbm_structures():
+ """
+ Load the WBM structures from a ASE DB file.
+ """
+ with connect(DATA_DIR.parent / "wbm_structures.db") as db:
+ for row in db.select():
+ yield row.toatoms(add_additional_information=True)
+
+def gather_results():
+ for model in MLIPEnum:
+ if "wbm_ev" not in REGISTRY[model.name].get("gpu-tasks", []):
+ continue
+
+ if (DATA_DIR / f"{model.name}.parquet").exists():
+ continue
+
+ all_data = []
+
+ for atoms in load_wbm_structures():
+ fpath = Path(model.name) / f"{atoms.info['key_value_pairs']['wbm_id']}.json"
+ if not fpath.exists():
+ continue
+
+ all_data.append(pd.read_json(fpath))
+
+ df = pd.concat(all_data, ignore_index=True)
+ df.to_parquet(DATA_DIR / f"{model.name}.parquet")
+
+
+def summarize():
+ summary_table = pd.DataFrame(
+ columns=[
+ "model",
+ "energy-diff-flip-times",
+ "tortuosity",
+ "spearman-compression-energy",
+ "spearman-compression-derivative",
+ "spearman-tension-energy",
+ "missing",
+ ]
+ )
+
+
+ for model in MLIPEnum:
+ fpath = DATA_DIR / f"{model.name}.parquet"
+ if not fpath.exists():
+ continue
+ df_raw_results = pd.read_parquet(fpath)
+
+ df_analyzed = pd.DataFrame(
+ columns=[
+ "model",
+ "structure",
+ "formula",
+ "volume-ratio",
+ "energy-delta-per-atom",
+ "energy-diff-flip-times",
+ "tortuosity",
+ "spearman-compression-energy",
+ "spearman-compression-derivative",
+ "spearman-tension-energy",
+ "missing",
+ ]
+ )
+
+ for wbm_struct in load_wbm_structures():
+ structure_id = wbm_struct.info["key_value_pairs"]["wbm_id"]
+
+ try:
+ results = df_raw_results.loc[df_raw_results["id"] == structure_id]
+ results = results["eos"].values[0]
+ es = np.array(results["energies"])
+ vols = np.array(results["volumes"])
+ vol0 = wbm_struct.get_volume()
+
+ indices = np.argsort(vols)
+ vols = vols[indices]
+ es = es[indices]
+
+ imine = len(es) // 2
+ # min_center_val = np.min(es[imid - 1 : imid + 2])
+ # imine = np.where(es == min_center_val)[0][0]
+ emin = es[imine]
+
+ interpolated_volumes = [
+ (vols[i] + vols[i + 1]) / 2 for i in range(len(vols) - 1)
+ ]
+ ediff = np.diff(es)
+ ediff_sign = np.sign(ediff)
+ mask = ediff_sign != 0
+ ediff = ediff[mask]
+ ediff_sign = ediff_sign[mask]
+ ediff_flip = np.diff(ediff_sign) != 0
+
+ etv = np.sum(np.abs(np.diff(es)))
+
+ data = {
+ "model": model.name,
+ "structure": structure_id,
+ "formula": wbm_struct.get_chemical_formula(),
+ "missing": False,
+ "volume-ratio": vols / vol0,
+ "energy-delta-per-atom": (es - emin) / len(wbm_struct),
+ "energy-diff-flip-times": np.sum(ediff_flip).astype(int),
+ "tortuosity": etv / (abs(es[0] - emin) + abs(es[-1] - emin)),
+ "spearman-compression-energy": stats.spearmanr(
+ vols[:imine], es[:imine]
+ ).statistic,
+ "spearman-compression-derivative": stats.spearmanr(
+ interpolated_volumes[:imine], ediff[:imine]
+ ).statistic,
+ "spearman-tension-energy": stats.spearmanr(
+ vols[imine:], es[imine:]
+ ).statistic,
+ }
+
+ except Exception:
+ data = {
+ "model": model.name,
+ "structure": structure_id,
+ "formula": wbm_struct.get_chemical_formula(),
+ "missing": True,
+ "volume-ratio": None,
+ "energy-delta-per-atom": None,
+ "energy-diff-flip-times": None,
+ "tortuosity": None,
+ "spearman-compression-energy": None,
+ "spearman-compression-derivative": None,
+ "spearman-tension-energy": None,
+ }
+
+ df_analyzed = pd.concat([df_analyzed, pd.DataFrame([data])], ignore_index=True)
+
+ df_analyzed.to_parquet(DATA_DIR / f"{model.name}_processed.parquet")
+ # json_fpath = DATA_DIR / f"EV_scan_analyzed_{model.name}.json"
+
+ # df_analyzed.to_json(json_fpath, orient="records")
+
+ valid_results = df_analyzed[df_analyzed["missing"] == False]
+
+ analysis_summary = {
+ "model": model.name,
+ "energy-diff-flip-times": valid_results["energy-diff-flip-times"].mean(),
+ "tortuosity": valid_results["tortuosity"].mean(),
+ "spearman-compression-energy": valid_results[
+ "spearman-compression-energy"
+ ].mean(),
+ "spearman-compression-derivative": valid_results[
+ "spearman-compression-derivative"
+ ].mean(),
+ "spearman-tension-energy": valid_results["spearman-tension-energy"].mean(),
+ "missing": len(df_analyzed[df_analyzed["missing"] == True]),
+ }
+ summary_table = pd.concat(
+ [summary_table, pd.DataFrame([analysis_summary])], ignore_index=True
+ )
+
+
+ flip_rank = (
+ (summary_table["energy-diff-flip-times"] - 1)
+ .abs()
+ .rank(ascending=True, method="min")
+ )
+ tortuosity_rank = summary_table["tortuosity"].rank(ascending=True, method="min")
+ spearman_compression_energy_rank = summary_table["spearman-compression-energy"].rank(
+ method="min"
+ )
+ spearman_compression_derivative_rank = summary_table[
+ "spearman-compression-derivative"
+ ].rank(ascending=False, method="min")
+ spearman_tension_energy_rank = summary_table["spearman-tension-energy"].rank(
+ ascending=False, method="min"
+ )
+ missing_rank = summary_table["missing"].rank(ascending=True, method="min")
+
+ rank_aggr = (
+ flip_rank
+ + tortuosity_rank
+ + spearman_compression_energy_rank
+ + spearman_compression_derivative_rank
+ + spearman_tension_energy_rank
+ + missing_rank
+ )
+ rank = rank_aggr.rank(method="min")
+
+ summary_table.insert(1, "rank", rank.astype(int))
+ summary_table.insert(2, "rank-aggregation", rank_aggr.astype(int))
+ summary_table = summary_table.sort_values(by="rank", ascending=True)
+ summary_table = summary_table.reset_index(drop=True)
+
+ summary_table.to_csv(DATA_DIR / "summary.csv", index=False)
+ summary_table.to_latex(DATA_DIR / "summary.tex", index=False)
+
+ return summary_table
+
+if __name__ == "__main__":
+ gather_results()
+ summarize()
diff --git a/benchmarks/wbm_ev/eSEN.parquet b/benchmarks/wbm_ev/eSEN.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..f81adce9b39dbdc5da9b4d46a439a8576fbf3254
--- /dev/null
+++ b/benchmarks/wbm_ev/eSEN.parquet
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+version https://git-lfs.github.com/spec/v1
+oid sha256:841febd80ec1024fa186ab05e5f9d7c96a0605d90c4840415ecf41ac89132aee
+size 410695
diff --git a/benchmarks/wbm_ev/eSEN_processed.parquet b/benchmarks/wbm_ev/eSEN_processed.parquet
new file mode 100644
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--- /dev/null
+++ b/benchmarks/wbm_ev/eSEN_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 356216
diff --git a/benchmarks/wbm_ev/eqV2(OMat).parquet b/benchmarks/wbm_ev/eqV2(OMat).parquet
new file mode 100644
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--- /dev/null
+++ b/benchmarks/wbm_ev/eqV2(OMat).parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7521597cd3189c0a3cea18ae98d9310bfc6becd50fca5e6f2c97af5e69b2596d
+size 414251
diff --git a/benchmarks/wbm_ev/eqV2(OMat)_processed.parquet b/benchmarks/wbm_ev/eqV2(OMat)_processed.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..8d9e3bda9c3d016535c3abd382a6605ccd7bf362
--- /dev/null
+++ b/benchmarks/wbm_ev/eqV2(OMat)_processed.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd43459abbcb6bf33b3dfecf131a6116d799f06aeb2b9e0a6c66e5d5db2ebdd6
+size 356817
diff --git a/benchmarks/wbm_ev/run.py b/benchmarks/wbm_ev/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..36baa25e3d84888227d71a87a895e7a29b8d69e9
--- /dev/null
+++ b/benchmarks/wbm_ev/run.py
@@ -0,0 +1,163 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from ase.db import connect
+from dask.distributed import Client
+from dask_jobqueue import SLURMCluster
+from prefect import flow, task
+from prefect.runtime import task_run
+from prefect_dask import DaskTaskRunner
+from prefect.cache_policies import INPUTS, TASK_SOURCE
+
+from mlip_arena.models import REGISTRY, MLIPEnum
+from mlip_arena.tasks.utils import get_calculator
+
+
+@task
+def load_wbm_structures():
+ """
+ Load the WBM structures from an ASE database file.
+
+ Reads structures from 'wbm_structures.db' and yields them as ASE Atoms objects
+ with additional metadata preserved from the database.
+
+ Yields:
+ ase.Atoms: Individual atomic structures from the WBM database with preserved
+ metadata in the .info dictionary.
+ """
+ with connect("../wbm_structures.db") as db:
+ for row in db.select():
+ yield row.toatoms(add_additional_information=True)
+
+@task(
+ name="E-V Scan",
+ task_run_name=lambda: f"{task_run.task_name}: {task_run.parameters['atoms'].get_chemical_formula()} - {task_run.parameters['model'].name}",
+ cache_policy=TASK_SOURCE + INPUTS,
+)
+def ev_scan(atoms, model):
+ """
+ Perform an energy-volume scan for a given model and atomic structure.
+
+ This function applies uniaxial strain to the structure in all three dimensions,
+ maintaining the fractional coordinates of atoms, and computes the energy at each
+ deformation point using the specified model.
+
+ Args:
+ atoms: ASE atoms object containing the structure to analyze.
+ model: MLIPEnum model to use for the energy calculations.
+
+ Returns:
+ dict: Results dictionary containing:
+ - method (str): The name of the model used
+ - id (str): The WBM ID of the structure
+ - eos (dict): Energy of state data with:
+ - volumes (list): Volume of the unit cell at each strain point
+ - energies (list): Computed potential energy at each strain point
+
+ Note:
+ The strain range is fixed at ยฑ20% with 21 evenly spaced points.
+ Results are also saved as a JSON file in a directory named after the model.
+ """
+ calculator = get_calculator(
+ model
+ ) # avoid sending entire model over prefect and select freer GPU
+
+ wbm_id = atoms.info["key_value_pairs"]["wbm_id"]
+
+ c0 = atoms.get_cell()
+ max_abs_strain = 0.2
+ npoints = 21
+ volumes = []
+ energies = []
+ for uniaxial_strain in np.linspace(-max_abs_strain, max_abs_strain, npoints):
+ cloned = atoms.copy()
+ scale_factor = uniaxial_strain + 1
+ cloned.set_cell(c0 * scale_factor, scale_atoms=True)
+ cloned.calc = calculator
+ volumes.append(cloned.get_volume())
+ energies.append(cloned.get_potential_energy())
+
+ data = {
+ "method": model.name,
+ "id": wbm_id,
+ "eos": {
+ "volumes": volumes, "energies": energies
+ }
+ }
+
+ fpath = Path(f"{model.name}") / f"{wbm_id}.json"
+ fpath.parent.mkdir(exist_ok=True)
+
+ df = pd.DataFrame([data])
+ df.to_json(fpath)
+
+ return df
+
+
+@flow
+def submit_tasks():
+ """
+ Create and submit energy-volume scan tasks for subsampled WBM structures and applicable models.
+
+ This flow function:
+ 1. Loads all structures from the WBM database
+ 2. Iterates through available models in MLIPEnum
+ 3. Filters models based on their capability to handle the 'wbm_ev' GPU task
+ 4. Submits parallel ev_scan tasks for all valid (structure, model) combinations
+ 5. Collects and returns results from all tasks
+
+ Returns:
+ list: Results from all executed tasks (successful or failed)
+ """
+ futures = []
+ for atoms in load_wbm_structures():
+ for model in MLIPEnum:
+ if "wbm_ev" not in REGISTRY[model.name].get("gpu-tasks", []):
+ continue
+ try:
+ result = ev_scan.submit(atoms, model)
+ except Exception as e:
+ print(f"Failed to submit task for {model.name}: {e}")
+ continue
+ futures.append(result)
+ return [f.result(raise_on_failure=False) for f in futures]
+
+if __name__ == "__main__":
+ nodes_per_alloc = 1
+ gpus_per_alloc = 1
+ ntasks = 1
+
+ cluster_kwargs = dict(
+ cores=1,
+ memory="64 GB",
+ processes=1,
+ shebang="#!/bin/bash",
+ account="m3828",
+ walltime="00:30:00",
+ # job_mem="0",
+ job_script_prologue=[
+ "source ~/.bashrc",
+ "module load python",
+ "source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena",
+ ],
+ job_directives_skip=["-n", "--cpus-per-task", "-J"],
+ job_extra_directives=[
+ "-J wbm_ev",
+ "-q debug",
+ f"-N {nodes_per_alloc}",
+ "-C gpu",
+ f"-G {gpus_per_alloc}",
+ "--exclusive",
+ ],
+ )
+
+ cluster = SLURMCluster(**cluster_kwargs)
+ print(cluster.job_script())
+ cluster.adapt(minimum_jobs=2, maximum_jobs=2)
+ client = Client(cluster)
+
+ submit_tasks.with_options(
+ task_runner=DaskTaskRunner(address=client.scheduler.address),
+ log_prints=True,
+ )()
diff --git a/benchmarks/wbm_ev/summary.csv b/benchmarks/wbm_ev/summary.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b71404c6d29423eef5b1932e81e6095929343b5c
--- /dev/null
+++ b/benchmarks/wbm_ev/summary.csv
@@ -0,0 +1,11 @@
+model,rank,rank-aggregation,energy-diff-flip-times,tortuosity,spearman-compression-energy,spearman-compression-derivative,spearman-tension-energy,missing
+eSEN,1,7,1.0,1.000402711291021,-0.9983393939393939,0.9999999999999999,0.9990454545454545,0
+MACE-MPA,2,14,1.0,1.000675741122765,-0.9983393939393939,0.9993090909090908,0.9987181818181818,0
+CHGNet,3,17,1.0,1.0006287770651048,-0.9982787878787878,0.9439636363636364,0.999090909090909,0
+MatterSim,4,24,1.009,1.000567338639546,-0.9980969696969696,0.9997090909090908,0.9937541835359507,0
+eqV2(OMat),5,27,1.035,1.0008346292192054,-0.9982060606060604,0.9972242424242423,0.9986454545454545,0
+M3GNet,6,29,1.002,1.0020010929112253,-0.9975878787878787,0.997442424242424,0.9964676571137886,0
+ORBv2,7,34,1.058,1.004064906459821,-0.9977696969696969,0.970751515151515,0.9976,0
+SevenNet,8,38,1.034,1.0100246177550205,-0.9951636363636364,0.9465575757575757,0.9947048195608054,0
+MACE-MP(M),9,40,1.121,1.0807128149289842,-0.9438060606060605,0.9011878787878788,0.9987454545454546,0
+ALIGNN,10,51,3.909,1.3756517739089669,-0.8892069391323368,0.7602706775644651,0.862085379002138,0
diff --git a/benchmarks/wbm_ev/summary.tex b/benchmarks/wbm_ev/summary.tex
new file mode 100644
index 0000000000000000000000000000000000000000..319047d96259572b129c9b0b9d2094231164dc65
--- /dev/null
+++ b/benchmarks/wbm_ev/summary.tex
@@ -0,0 +1,16 @@
+\begin{tabular}{lrrrrrrrl}
+\toprule
+model & rank & rank-aggregation & energy-diff-flip-times & tortuosity & spearman-compression-energy & spearman-compression-derivative & spearman-tension-energy & missing \\
+\midrule
+eSEN & 1 & 7 & 1.000000 & 1.000403 & -0.998339 & 1.000000 & 0.999045 & 0 \\
+MACE-MPA & 2 & 14 & 1.000000 & 1.000676 & -0.998339 & 0.999309 & 0.998718 & 0 \\
+CHGNet & 3 & 17 & 1.000000 & 1.000629 & -0.998279 & 0.943964 & 0.999091 & 0 \\
+MatterSim & 4 & 24 & 1.009000 & 1.000567 & -0.998097 & 0.999709 & 0.993754 & 0 \\
+eqV2(OMat) & 5 & 27 & 1.035000 & 1.000835 & -0.998206 & 0.997224 & 0.998645 & 0 \\
+M3GNet & 6 & 29 & 1.002000 & 1.002001 & -0.997588 & 0.997442 & 0.996468 & 0 \\
+ORBv2 & 7 & 34 & 1.058000 & 1.004065 & -0.997770 & 0.970752 & 0.997600 & 0 \\
+SevenNet & 8 & 38 & 1.034000 & 1.010025 & -0.995164 & 0.946558 & 0.994705 & 0 \\
+MACE-MP(M) & 9 & 40 & 1.121000 & 1.080713 & -0.943806 & 0.901188 & 0.998745 & 0 \\
+ALIGNN & 10 & 51 & 3.909000 & 1.375652 & -0.889207 & 0.760271 & 0.862085 & 0 \\
+\bottomrule
+\end{tabular}
diff --git a/benchmarks/wbm_structures.db b/benchmarks/wbm_structures.db
new file mode 100644
index 0000000000000000000000000000000000000000..e1f15c8aa5b8d8fd27b2a9b9ce833deed609f476
--- /dev/null
+++ b/benchmarks/wbm_structures.db
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc387c7787c21e7ff2ab80d5428c60b9e817c9b37f53e03c0f5e5e72dc44fe88
+size 782336
diff --git a/mlip_arena/__init__.py b/mlip_arena/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f4ccde16b3b106b00f8e7824bc351b81314c603
--- /dev/null
+++ b/mlip_arena/__init__.py
@@ -0,0 +1,3 @@
+from pathlib import Path
+
+PKG_DIR = Path(__file__).parent
diff --git a/mlip_arena/data/__init__.py b/mlip_arena/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mlip_arena/data/collate.py b/mlip_arena/data/collate.py
new file mode 100644
index 0000000000000000000000000000000000000000..484cbacd79ce599170f4b5bfff96dc53f8c1dd92
--- /dev/null
+++ b/mlip_arena/data/collate.py
@@ -0,0 +1,201 @@
+import numpy as np
+import torch
+
+# TODO: consider using vesin
+from matscipy.neighbours import neighbour_list
+from torch_geometric.data import Data
+
+from ase import Atoms
+from ase.calculators.singlepoint import SinglePointCalculator
+
+
+def get_neighbor(
+ atoms: Atoms, cutoff: float, self_interaction: bool = False
+):
+ pbc = atoms.pbc
+ cell = atoms.cell.array
+
+ i, j, S = neighbour_list(
+ quantities="ijS",
+ pbc=pbc,
+ cell=cell,
+ positions=atoms.positions,
+ cutoff=cutoff
+ )
+
+ if not self_interaction:
+ # Eliminate self-edges that don't cross periodic boundaries
+ true_self_edge = i == j
+ true_self_edge &= np.all(S == 0, axis=1)
+ keep_edge = ~true_self_edge
+
+ i = i[keep_edge]
+ j = j[keep_edge]
+ S = S[keep_edge]
+
+ edge_index = np.stack((i, j)).astype(np.int64)
+ edge_shift = np.dot(S, cell)
+
+ return edge_index, edge_shift
+
+
+
+def collate_fn(batch: list[Atoms], cutoff: float) -> Data:
+ """Collate a list of Atoms objects into a single batched Atoms object."""
+
+ # Offset the edge indices for each graph to ensure they remain disconnected
+ offset = 0
+
+ node_batch = []
+
+ numbers_batch = []
+ positions_batch = []
+ # ec_batch = []
+
+ forces_batch = []
+ charges_batch = []
+ magmoms_batch = []
+ dipoles_batch = []
+
+ edge_index_batch = []
+ edge_shift_batch = []
+
+ cell_batch = []
+ natoms_batch = []
+
+ energy_batch = []
+ stress_batch = []
+
+ for i, atoms in enumerate(batch):
+
+ edge_index, edge_shift = get_neighbor(atoms, cutoff=cutoff, self_interaction=False)
+
+ edge_index[0] += offset
+ edge_index[1] += offset
+ edge_index_batch.append(torch.tensor(edge_index))
+ edge_shift_batch.append(torch.tensor(edge_shift))
+
+ natoms = len(atoms)
+ offset += natoms
+ node_batch.append(torch.ones(natoms, dtype=torch.long) * i)
+ natoms_batch.append(natoms)
+
+ cell_batch.append(torch.tensor(atoms.cell.array))
+ numbers_batch.append(torch.tensor(atoms.numbers))
+ positions_batch.append(torch.tensor(atoms.positions))
+
+ # ec_batch.append([Atom(int(a)).elecronic_encoding for a in atoms.numbers])
+
+ charges_batch.append(
+ atoms.get_initial_charges()
+ if atoms.get_initial_charges().any()
+ else torch.full((natoms,), torch.nan)
+ )
+ magmoms_batch.append(
+ atoms.get_initial_magnetic_moments()
+ if atoms.get_initial_magnetic_moments().any()
+ else torch.full((natoms,), torch.nan)
+ )
+
+ # Create the new 'arrays' data for the batch
+
+ cell_batch = torch.stack(cell_batch, dim=0)
+ node_batch = torch.cat(node_batch, dim=0)
+ positions_batch = torch.cat(positions_batch, dim=0)
+ numbers_batch = torch.cat(numbers_batch, dim=0)
+ natoms_batch = torch.tensor(natoms_batch, dtype=torch.long)
+
+ charges_batch = torch.cat(charges_batch, dim=0) if charges_batch else None
+ magmoms_batch = torch.cat(magmoms_batch, dim=0) if magmoms_batch else None
+
+ # ec_batch = list(map(lambda a: Atom(int(a)).elecronic_encoding, numbers_batch))
+ # ec_batch = torch.stack(ec_batch, dim=0)
+
+ edge_index_batch = torch.cat(edge_index_batch, dim=1)
+ edge_shift_batch = torch.cat(edge_shift_batch, dim=0)
+
+ arrays_batch_concatenated = {
+ "cell": cell_batch,
+ "positions": positions_batch,
+ "edge_index": edge_index_batch,
+ "edge_shift": edge_shift_batch,
+ "numbers": numbers_batch,
+ "num_nodes": offset,
+ "batch": node_batch,
+ "charges": charges_batch,
+ "magmoms": magmoms_batch,
+ # "ec": ec_batch,
+ "natoms": natoms_batch,
+ "cutoff": torch.tensor(cutoff),
+ }
+
+ # TODO: custom fields
+
+ # Create a new Data object with the concatenated arrays data
+ batch_data = Data.from_dict(arrays_batch_concatenated)
+
+ return batch_data
+
+
+def decollate_fn(batch_data: Data) -> list[Atoms]:
+ """Decollate a batched Data object into a list of individual Atoms objects."""
+
+ # FIXME: this function is not working properly when the batch_data is on GPU.
+ # TODO: create a new Cell class using torch tensor to handle device placement.
+ # As a temporary fix, detach the batch_data from the GPU and move it to CPU.
+ batch_data = batch_data.detach().cpu()
+
+ # Initialize empty lists to store individual data entries
+ individual_entries = []
+
+ # Split the 'batch' attribute to identify data entries
+ unique_batches = batch_data.batch.unique(sorted=True)
+
+ for i in unique_batches:
+ # Identify the indices corresponding to the current data entry
+ entry_indices = (batch_data.batch == i).nonzero(as_tuple=True)[0]
+
+ # Extract the attributes for the current data entry
+ cell = batch_data.cell[i]
+ numbers = batch_data.numbers[entry_indices]
+ positions = batch_data.positions[entry_indices]
+ # edge_index = batch_data.edge_index[:, entry_indices]
+ # edge_shift = batch_data.edge_shift[entry_indices]
+ # batch_data.ec[entry_indices] if batch_data.ec is not None else None
+
+ # Optional fields
+ energy = batch_data.energy[i] if "energy" in batch_data else None
+ forces = batch_data.forces[entry_indices] if "forces" in batch_data else None
+ stress = batch_data.stress[i] if "stress" in batch_data else None
+
+ # charges = batch_data.charges[entry_indices] if "charges" in batch_data else None
+ # magmoms = batch_data.magmoms[entry_indices] if "magmoms" in batch_data else None
+ # dipoles = batch_data.dipoles[entry_indices] if "dipoles" in batch_data else None
+
+ # TODO: cumstom fields
+
+ # Create an 'Atoms' object for the current data entry
+ atoms = Atoms(
+ cell=cell,
+ positions=positions,
+ numbers=numbers,
+ # forces=None if torch.any(torch.isnan(forces)) else forces,
+ # charges=None if torch.any(torch.isnan(charges)) else charges,
+ # magmoms=None if torch.any(torch.isnan(magmoms)) else magmoms,
+ # dipoles=None if torch.any(torch.isnan(dipoles)) else dipoles,
+ # energy=None if torch.isnan(energy) else energy,
+ # stress=None if torch.any(torch.isnan(stress)) else stress,
+ )
+
+ atoms.calc = SinglePointCalculator(
+ energy=energy,
+ forces=forces,
+ stress=stress,
+ # charges=charges,
+ # magmoms=magmoms,
+ ) # type: ignore
+
+ # Append the individual data entry to the list
+ individual_entries.append(atoms)
+
+ return individual_entries
diff --git a/mlip_arena/data/local.py b/mlip_arena/data/local.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d890e43541bbac154477aa7d61fe7e2784dcdb0
--- /dev/null
+++ b/mlip_arena/data/local.py
@@ -0,0 +1,25 @@
+import os
+import time
+
+from pandas import HDFStore
+
+# https://stackoverflow.com/questions/22522551/pandas-hdf5-as-a-database/29014295#29014295
+
+
+class SafeHDFStore(HDFStore):
+ def __init__(self, *args, **kwargs):
+ probe_interval = kwargs.pop("probe_interval", 1)
+ self._lock = "%s.lock" % args[0]
+ while True:
+ try:
+ self._flock = os.open(self._lock, os.O_CREAT | os.O_EXCL | os.O_WRONLY)
+ break
+ except FileExistsError:
+ time.sleep(probe_interval)
+
+ HDFStore.__init__(self, *args, **kwargs)
+
+ def __exit__(self, *args, **kwargs):
+ HDFStore.__exit__(self, *args, **kwargs)
+ os.close(self._flock)
+ os.remove(self._lock)
diff --git a/mlip_arena/jobs/__init__.py b/mlip_arena/jobs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..09d56bd40f161ce2e9bf651a77eebf5581766210
--- /dev/null
+++ b/mlip_arena/jobs/__init__.py
@@ -0,0 +1,38 @@
+
+import enum
+
+from mlip_arena.models import MLIP
+from mlip_arena.tasks import Task
+
+
+class Machine(enum.Enum):
+ """Enum class for machine"""
+ HFCPU = "Hugging Face CPU Basic"
+ PERLCPU = "NERSC Perlmutter CPU"
+ PERLA100 = "NERSC Perlmutter A100 40GB"
+ PERLA100L = "NERSC Perlmutter A100 80GB"
+
+class Job:
+ def __init__(self, model: MLIP, task: Task, machine: Machine, **kwargs):
+ self.calculator = model
+ self.task = task
+ self.machine = machine
+ self.kwargs = kwargs
+
+ def __str__(self):
+ return f"Job: {self.task.name} on {self.machine.value}"
+
+ def run(self):
+ if self.machine == Machine.HFCPU:
+ print(f"Running {self.name} on {self.machine.value}")
+ "run the task on Hugging Face CPU Basic"
+ raise NotImplementedError
+ elif self.machine == Machine.PERLCPU:
+ print(f"Running {self.name} on {self.machine.value}")
+ "send the task to NERSC Perlmutter CPU node and listen for the results"
+ raise NotImplementedError
+ elif self.machine == Machine.PERLA100:
+ print(f"Running {self.name} on {self.machine.value}")
+ "send the task to NERSC Perlmutter GPU node and listen for the results"
+ raise NotImplementedError
+
\ No newline at end of file
diff --git a/mlip_arena/jobs/run.py b/mlip_arena/jobs/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cf3c4600ba55b1f46e7e7e7d9fd5f46cd2bd07b
--- /dev/null
+++ b/mlip_arena/jobs/run.py
@@ -0,0 +1,13 @@
+import importlib
+
+from mlip_arena.models import REGISTRY as MODEL_REGISTRY
+from mlip_arena.tasks import REGISTRY as TASK_REGISTRY
+
+print(MODEL_REGISTRY)
+print(TASK_REGISTRY)
+
+for task, metadata in TASK_REGISTRY.items():
+
+ print(f"mlip_arena.tasks.{task}")
+ module = importlib.import_module(f"mlip_arena.tasks.{task}")
+ module.whoami()
\ No newline at end of file
diff --git a/mlip_arena/models/README.md b/mlip_arena/models/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a338026c81874fd524da2908e064b616ff88c722
--- /dev/null
+++ b/mlip_arena/models/README.md
@@ -0,0 +1,9 @@
+
+
+## Note on model registration
+
+1. Use `ast` to parse model classes from the uploaded script.
+2. Add the classes and their supported tasks to the model registry file `registry.yaml`.
+3. Run tests on HF Space to ensure the model is working as expected.
+4. [Push files to the Hub](https://huggingface.co/docs/huggingface_hub/guides/upload) and sync with github repository.
+5. Use [HF webhook](https://huggingface.co/docs/hub/en/webhooks) to check the status of benchmark tasks (pass, fail, null), run unfinisehd tasks and visualize the results on leaderboard. [[guide]](https://huggingface.co/docs/hub/en/webhooks-guide-metadata-review)
\ No newline at end of file
diff --git a/mlip_arena/models/__init__.py b/mlip_arena/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..775899e98efcb12efe99a0163f358d2acdb0c7dd
--- /dev/null
+++ b/mlip_arena/models/__init__.py
@@ -0,0 +1,171 @@
+from __future__ import annotations
+
+import importlib
+from enum import Enum
+from pathlib import Path
+from typing import Dict, Optional, Type, TypeVar, Union
+
+T = TypeVar("T", bound="MLIP")
+
+import torch
+import yaml
+from ase import Atoms
+from ase.calculators.calculator import Calculator, all_changes
+from huggingface_hub import PyTorchModelHubMixin
+from torch import nn
+from typing_extensions import Self
+
+try:
+ from mlip_arena.data.collate import collate_fn
+except ImportError:
+ # Fallback to a dummy function if the import fails
+ def collate_fn(batch: list[Atoms], cutoff: float) -> None:
+ raise ImportError(
+ "collate_fn import failed. Please install the required dependencies."
+ )
+
+try:
+ from prefect.logging import get_run_logger
+
+ logger = get_run_logger()
+except (ImportError, RuntimeError):
+ from loguru import logger
+
+with open(Path(__file__).parent / "registry.yaml", encoding="utf-8") as f:
+ REGISTRY = yaml.safe_load(f)
+
+MLIPMap = {}
+
+for model, metadata in REGISTRY.items():
+ try:
+ module = importlib.import_module(
+ f"{__package__}.{metadata['module']}.{metadata['family']}"
+ )
+ MLIPMap[model] = getattr(module, metadata["class"])
+ except (ModuleNotFoundError, AttributeError, ValueError, ImportError, Exception) as e:
+ logger.warning(e)
+ continue
+
+MLIPEnum = Enum("MLIPEnum", MLIPMap)
+
+
+class MLIP(
+ nn.Module,
+ PyTorchModelHubMixin,
+ tags=["atomistic-simulation", "MLIP"],
+):
+ def __init__(self, model: nn.Module) -> None:
+ super().__init__()
+ # https://github.com/pytorch/pytorch/blob/3cbc8c54fd37eb590e2a9206aecf3ab568b3e63c/torch/_dynamo/config.py#L534
+ # torch._dynamo.config.compiled_autograd = True
+ # self.model = torch.compile(model)
+ self.model = model
+
+ def _save_pretrained(self, save_directory: Path) -> None:
+ return super()._save_pretrained(save_directory)
+
+ @classmethod
+ def from_pretrained(
+ cls,
+ pretrained_model_name_or_path: str | Path,
+ *,
+ force_download: bool = False,
+ resume_download: bool | None = None,
+ proxies: dict | None = None,
+ token: str | bool | None = None,
+ cache_dir: str | Path | None = None,
+ local_files_only: bool = False,
+ revision: str | None = None,
+ **model_kwargs,
+ ) -> Self:
+ return super().from_pretrained(
+ pretrained_model_name_or_path,
+ force_download=force_download,
+ resume_download=resume_download,
+ proxies=proxies,
+ token=token,
+ cache_dir=cache_dir,
+ local_files_only=local_files_only,
+ revision=revision,
+ **model_kwargs,
+ )
+
+ def forward(self, x):
+ return self.model(x)
+
+
+class MLIPCalculator(MLIP, Calculator):
+ name: str
+ implemented_properties: list[str] = ["energy", "forces", "stress"]
+
+ def __init__(
+ self,
+ model: nn.Module,
+ device: torch.device | None = None,
+ cutoff: float = 6.0,
+ # ASE Calculator
+ restart=None,
+ atoms=None,
+ directory=".",
+ calculator_kwargs: dict = {},
+ ):
+ MLIP.__init__(self, model=model) # Initialize MLIP part
+ Calculator.__init__(
+ self, restart=restart, atoms=atoms, directory=directory, **calculator_kwargs
+ ) # Initialize ASE Calculator part
+ # Additional initialization if needed
+ # self.name: str = self.__class__.__name__
+ from mlip_arena.models.utils import get_freer_device
+
+ self.device = device or get_freer_device()
+ self.cutoff = cutoff
+ self.model.to(self.device)
+ # self.device = device or torch.device(
+ # "cuda" if torch.cuda.is_available() else "cpu"
+ # )
+ # self.model: MLIP = MLIP.from_pretrained(model_path, map_location=self.device)
+ # self.implemented_properties = ["energy", "forces", "stress"]
+
+ # def __getstate__(self):
+ # state = self.__dict__.copy()
+ # state["_modules"]["model"] = state["_modules"]["model"]._orig_mod
+ # return state
+
+ # def __setstate__(self, state):
+ # self.__dict__.update(state)
+ # self.model = torch.compile(state["_modules"]["model"])
+
+ def calculate(
+ self,
+ atoms: Atoms,
+ properties: list[str],
+ system_changes: list = all_changes,
+ ):
+ """Calculate energies and forces for the given Atoms object"""
+ super().calculate(atoms, properties, system_changes)
+
+ # TODO: move collate_fn to here in MLIPCalculator
+ data = collate_fn([atoms], cutoff=self.cutoff).to(self.device)
+ output = self.forward(data)
+
+ # TODO: decollate_fn
+
+ self.results = {}
+ if "energy" in properties:
+ self.results["energy"] = output["energy"].squeeze().item()
+ if "forces" in properties:
+ self.results["forces"] = output["forces"].squeeze().cpu().detach().numpy()
+ if "stress" in properties:
+ self.results["stress"] = output["stress"].squeeze().cpu().detach().numpy()
+
+ # def forward(self, x: Atoms) -> dict[str, torch.Tensor]:
+ # """Implement data conversion, graph creation, and model forward pass
+
+ # Example implementation:
+ # 1. Use `ase.neighborlist.NeighborList` to get neighbor list
+ # 2. Create `torch_geometric.data.Data` object and copy the data
+ # 3. Pass the `Data` object to the model and return the output
+
+ # """
+
+ # raise NotImplementedError
diff --git a/mlip_arena/models/classicals/__init__.py b/mlip_arena/models/classicals/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mlip_arena/models/classicals/zbl.py b/mlip_arena/models/classicals/zbl.py
new file mode 100644
index 0000000000000000000000000000000000000000..5821539f1565fa0506957fe7b9fb2351954d9d0a
--- /dev/null
+++ b/mlip_arena/models/classicals/zbl.py
@@ -0,0 +1,214 @@
+import torch
+import torch.linalg as LA
+import torch.nn as nn
+import torch_scatter
+from torch_geometric.data import Data
+
+from ase.data import covalent_radii
+from ase.units import _e, _eps0, m, pi
+from e3nn.util.jit import compile_mode # TODO: e3nn allows autograd in compiled model
+
+
+@compile_mode("script")
+class ZBL(nn.Module):
+ """Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion"""
+
+ def __init__(
+ self,
+ trianable: bool = False,
+ **kwargs,
+ ) -> None:
+ nn.Module.__init__(self, **kwargs)
+
+ torch.set_default_dtype(torch.double)
+
+ self.a = torch.nn.parameter.Parameter(
+ torch.tensor(
+ [0.18175, 0.50986, 0.28022, 0.02817], dtype=torch.get_default_dtype()
+ ),
+ requires_grad=trianable,
+ )
+ self.b = torch.nn.parameter.Parameter(
+ torch.tensor(
+ [-3.19980, -0.94229, -0.40290, -0.20162],
+ dtype=torch.get_default_dtype(),
+ ),
+ requires_grad=trianable,
+ )
+
+ self.a0 = torch.nn.parameter.Parameter(
+ torch.tensor(0.46850, dtype=torch.get_default_dtype()),
+ requires_grad=trianable,
+ )
+
+ self.p = torch.nn.parameter.Parameter(
+ torch.tensor(0.23, dtype=torch.get_default_dtype()), requires_grad=trianable
+ )
+
+ self.register_buffer(
+ "covalent_radii",
+ torch.tensor(
+ covalent_radii,
+ dtype=torch.get_default_dtype(),
+ ),
+ )
+
+ def phi(self, x):
+ return torch.einsum("i,ij->j", self.a, torch.exp(torch.outer(self.b, x)))
+
+ def d_phi(self, x):
+ return torch.einsum(
+ "i,ij->j", self.a * self.b, torch.exp(torch.outer(self.b, x))
+ )
+
+ def dd_phi(self, x):
+ return torch.einsum(
+ "i,ij->j", self.a * self.b**2, torch.exp(torch.outer(self.b, x))
+ )
+
+ def eij(
+ self, zi: torch.Tensor, zj: torch.Tensor, rij: torch.Tensor
+ ) -> torch.Tensor: # [eV]
+ return _e * m / (4 * pi * _eps0) * torch.div(torch.mul(zi, zj), rij)
+
+ def d_eij(
+ self, zi: torch.Tensor, zj: torch.Tensor, rij: torch.Tensor
+ ) -> torch.Tensor: # [eV / A]
+ return -_e * m / (4 * pi * _eps0) * torch.div(torch.mul(zi, zj), rij**2)
+
+ def dd_eij(
+ self, zi: torch.Tensor, zj: torch.Tensor, rij: torch.Tensor
+ ) -> torch.Tensor: # [eV / A^2]
+ return _e * m / (2 * pi * _eps0) * torch.div(torch.mul(zi, zj), rij**3)
+
+ def switch_fn(
+ self,
+ zi: torch.Tensor,
+ zj: torch.Tensor,
+ rij: torch.Tensor,
+ aij: torch.Tensor,
+ router: torch.Tensor,
+ rinner: torch.Tensor,
+ ) -> torch.Tensor: # [eV]
+ # aij = self.a0 / (torch.pow(zi, self.p) + torch.pow(zj, self.p))
+
+ xrouter = router / aij
+
+ energy = self.eij(zi, zj, router) * self.phi(xrouter)
+
+ grad1 = self.d_eij(zi, zj, router) * self.phi(xrouter) + self.eij(
+ zi, zj, router
+ ) * self.d_phi(xrouter)
+
+ grad2 = (
+ self.dd_eij(zi, zj, router) * self.phi(xrouter)
+ + self.d_eij(zi, zj, router) * self.d_phi(xrouter)
+ + self.d_eij(zi, zj, router) * self.d_phi(xrouter)
+ + self.eij(zi, zj, router) * self.dd_phi(xrouter)
+ )
+
+ A = (-3 * grad1 + (router - rinner) * grad2) / (router - rinner) ** 2
+ B = (2 * grad1 - (router - rinner) * grad2) / (router - rinner) ** 3
+ C = (
+ -energy
+ + 1.0 / 2.0 * (router - rinner) * grad1
+ - 1.0 / 12.0 * (router - rinner) ** 2 * grad2
+ )
+
+ switching = torch.where(
+ rij < rinner,
+ C,
+ A / 3.0 * (rij - rinner) ** 3 + B / 4.0 * (rij - rinner) ** 4 + C,
+ )
+
+ return switching
+
+ def envelope(self, r: torch.Tensor, rc: torch.Tensor, p: int = 6):
+ x = r / rc
+ y = (
+ 1.0
+ - ((p + 1.0) * (p + 2.0) / 2.0) * torch.pow(x, p)
+ + p * (p + 2.0) * torch.pow(x, p + 1)
+ - (p * (p + 1.0) / 2) * torch.pow(x, p + 2)
+ ) * (x < 1)
+ return y
+
+ def _get_derivatives(self, energy: torch.Tensor, data: Data):
+ egradi, egradij = torch.autograd.grad(
+ outputs=[energy], # TODO: generalized derivatives
+ inputs=[data.positions, data.vij], # TODO: generalized derivatives
+ grad_outputs=[torch.ones_like(energy)],
+ retain_graph=True,
+ create_graph=True,
+ allow_unused=True,
+ )
+
+ volume = torch.det(data.cell) # (batch,)
+ rfaxy = torch.einsum("ax,ay->axy", data.vij, -egradij)
+
+ edge_batch = data.batch[data.edge_index[0]]
+
+ stress = (
+ -0.5
+ * torch_scatter.scatter_sum(rfaxy, edge_batch, dim=0)
+ / volume.view(-1, 1)
+ )
+
+ return -egradi, stress
+
+ def forward(
+ self,
+ data: Data,
+ ) -> dict[str, torch.Tensor]:
+ # TODO: generalized derivatives
+ data.positions.requires_grad_(True)
+
+ numbers = data.numbers # (sum(N), )
+ positions = data.positions # (sum(N), 3)
+ edge_index = data.edge_index # (2, sum(E))
+ edge_shift = data.edge_shift # (sum(E), 3)
+ batch = data.batch # (sum(N), )
+
+ edge_src, edge_dst = edge_index[0], edge_index[1]
+
+ if "rij" not in data or "vij" not in data:
+ data.vij = positions[edge_dst] - positions[edge_src] + edge_shift
+ data.rij = LA.norm(data.vij, dim=-1)
+
+ rbond = (
+ self.covalent_radii[numbers[edge_src]]
+ + self.covalent_radii[numbers[edge_dst]]
+ )
+
+ rij = data.rij
+ zi = numbers[edge_src] # (sum(E), )
+ zj = numbers[edge_dst] # (sum(E), )
+
+ aij = self.a0 / (torch.pow(zi, self.p) + torch.pow(zj, self.p)) # (sum(E), )
+
+ energy_pairs = (
+ self.eij(zi, zj, rij)
+ * self.phi(rij / aij.to(rij))
+ * self.envelope(rij, torch.min(data.cutoff, rbond))
+ )
+
+ energy_nodes = 0.5 * torch_scatter.scatter_add(
+ src=energy_pairs,
+ index=edge_dst,
+ dim=0,
+ ) # (sum(N), )
+
+ energies = torch_scatter.scatter_add(
+ src=energy_nodes,
+ index=batch,
+ dim=0,
+ ) # (B, )
+
+ # TODO: generalized derivatives
+ forces, stress = self._get_derivatives(energies, data)
+
+ return {
+ "energy": energies,
+ "forces": forces,
+ "stress": stress,
+ }
diff --git a/mlip_arena/models/externals/__init__.py b/mlip_arena/models/externals/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mlip_arena/models/externals/alignn.py b/mlip_arena/models/externals/alignn.py
new file mode 100644
index 0000000000000000000000000000000000000000..5741587d043a00de668b69d4980dff5a1b0d41f1
--- /dev/null
+++ b/mlip_arena/models/externals/alignn.py
@@ -0,0 +1,15 @@
+from __future__ import annotations
+
+from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+
+from mlip_arena.models.utils import get_freer_device
+
+
+class ALIGNN(AlignnAtomwiseCalculator):
+ def __init__(self, device=None, **kwargs) -> None:
+ # TODO: cannot control version
+ # _ = get_figshare_model_ff(dir_path=dir_path)
+ model_path = default_path()
+
+ device = device or get_freer_device()
+ super().__init__(path=model_path, device=device, **kwargs)
diff --git a/mlip_arena/models/externals/ani.py b/mlip_arena/models/externals/ani.py
new file mode 100644
index 0000000000000000000000000000000000000000..31da73c17214daa395c9ff67f186353c031adbc3
--- /dev/null
+++ b/mlip_arena/models/externals/ani.py
@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+import yaml
+from pathlib import Path
+
+from ase.calculators.calculator import all_changes
+from torchani.ase import Calculator as ANICalculator
+from torchani.models import BuiltinEnsemble
+
+from mlip_arena.models.utils import get_freer_device
+
+
+
+with open(Path(__file__).parents[1] / "registry.yaml", encoding="utf-8") as f:
+ REGISTRY = yaml.safe_load(f)
+
+
+class ANI2x(ANICalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["ANI2x"]["checkpoint"],
+ device: str | None = None,
+ periodic_table_index=False,
+ **kwargs,
+ ):
+ self.device = device or str(get_freer_device())
+
+ ensemble = BuiltinEnsemble._from_neurochem_resources(
+ checkpoint, periodic_table_index
+ )
+ # TODO: ANICalculator does not offer API to change device
+ # ensemble.species.device = self.device
+ super().__init__(ensemble.species, ensemble, **kwargs)
+
+
+ def calculate(
+ self, atoms=None, properties=['energy', 'forces', 'stress'], system_changes=all_changes
+ ):
+ super().calculate(atoms, properties, system_changes)
diff --git a/mlip_arena/models/externals/chgnet.py b/mlip_arena/models/externals/chgnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..dac62185718c32de0a32b5184f882d19c8419b99
--- /dev/null
+++ b/mlip_arena/models/externals/chgnet.py
@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from typing import Literal
+
+from ase import Atoms
+from chgnet.model.dynamics import CHGNetCalculator
+from chgnet.model.model import CHGNet as CHGNetModel
+
+from mlip_arena.models.utils import get_freer_device
+
+
+class CHGNet(CHGNetCalculator):
+ def __init__(
+ self,
+ checkpoint: CHGNetModel | None = None, # TODO: specifiy version
+ device: str | None = None,
+ stress_weight: float | None = 1 / 160.21766208,
+ on_isolated_atoms: Literal["ignore", "warn", "error"] = "warn",
+ **kwargs,
+ ) -> None:
+ use_device = str(device or get_freer_device())
+ super().__init__(
+ model=checkpoint,
+ use_device=use_device,
+ stress_weight=stress_weight,
+ on_isolated_atoms=on_isolated_atoms,
+ **kwargs,
+ )
+
+ def calculate(
+ self,
+ atoms: Atoms | None = None,
+ properties: list | None = None,
+ system_changes: list | None = None,
+ ) -> None:
+ super().calculate(atoms, properties, system_changes)
+
+ # for ase.io.write compatibility
+ self.results.pop("crystal_fea", None)
diff --git a/mlip_arena/models/externals/deepmd.py b/mlip_arena/models/externals/deepmd.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c5a4e4d96807ec7f09a48db8033ec5fd69bc6c8
--- /dev/null
+++ b/mlip_arena/models/externals/deepmd.py
@@ -0,0 +1,47 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import yaml
+import requests
+from deepmd.calculator import DP as DPCalculator
+
+from mlip_arena.models.utils import get_freer_device
+
+with open(Path(__file__).parents[1] / "registry.yaml", encoding="utf-8") as f:
+ REGISTRY = yaml.safe_load(f)
+
+class DeepMD(DPCalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["DeepMD"]["checkpoint"],
+ device=None,
+ **kwargs,
+ ):
+ device = device or get_freer_device()
+
+ cache_dir = Path.home() / ".cache" / "deepmd"
+ cache_dir.mkdir(parents=True, exist_ok=True)
+ model_path = cache_dir / checkpoint
+
+ url = "https://bohrium-api.dp.tech/ds-dl/mlip-arena-tfpk-v1.zip"
+
+ if not model_path.exists():
+ import zipfile
+
+ print(f"Downloading DeepMD model from {url} to {model_path}...")
+ try:
+ response = requests.get(url, stream=True, timeout=120)
+ response.raise_for_status()
+ with open(cache_dir/"temp.zip", "wb") as f:
+ for chunk in response.iter_content(chunk_size=8192):
+ f.write(chunk)
+ print("Download completed.")
+ with zipfile.ZipFile(cache_dir/"temp.zip", "r") as zip_ref:
+ zip_ref.extractall(cache_dir)
+ print("Unzip completed.")
+ except requests.exceptions.RequestException as e:
+ raise RuntimeError("Failed to download DeepMD model.") from e
+
+
+ super().__init__(model_path, device=device, **kwargs)
\ No newline at end of file
diff --git a/mlip_arena/models/externals/equiformer.py b/mlip_arena/models/externals/equiformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..c023b9f278bd66741e6683014944248e5861a190
--- /dev/null
+++ b/mlip_arena/models/externals/equiformer.py
@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import yaml
+from ase import Atoms
+from fairchem.core import OCPCalculator
+
+with open(Path(__file__).parents[1] / "registry.yaml", encoding="utf-8") as f:
+ REGISTRY = yaml.safe_load(f)
+
+
+class EquiformerV2(OCPCalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["EquiformerV2(OC22)"]["checkpoint"],
+ # TODO: cannot assign device
+ local_cache="/tmp/ocp/",
+ cpu=False,
+ seed=0,
+ **kwargs,
+ ) -> None:
+ kwargs.pop("device", None)
+ super().__init__(
+ model_name=checkpoint,
+ local_cache=local_cache,
+ cpu=cpu,
+ seed=seed,
+ **kwargs,
+ )
+
+ def calculate(self, atoms: Atoms, properties, system_changes) -> None:
+ super().calculate(atoms, properties, system_changes)
+
+ self.results.update(
+ force=atoms.get_forces(),
+ )
+
+
+class EquiformerV2OC20(OCPCalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["EquiformerV2(OC22)"]["checkpoint"],
+ # TODO: cannot assign device
+ local_cache="/tmp/ocp/",
+ cpu=False,
+ seed=0,
+ **kwargs,
+ ) -> None:
+ super().__init__(
+ model_name=checkpoint,
+ local_cache=local_cache,
+ cpu=cpu,
+ seed=seed,
+ **kwargs,
+ )
\ No newline at end of file
diff --git a/mlip_arena/models/externals/escn.py b/mlip_arena/models/externals/escn.py
new file mode 100644
index 0000000000000000000000000000000000000000..7aa8390f90c126078b48b12dc9437a02d75285b7
--- /dev/null
+++ b/mlip_arena/models/externals/escn.py
@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import yaml
+from ase import Atoms
+from fairchem.core import OCPCalculator
+
+with open(Path(__file__).parents[1] / "registry.yaml", encoding="utf-8") as f:
+ REGISTRY = yaml.safe_load(f)
+
+class eSCN(OCPCalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["eSCN(OC20)"]["checkpoint"], # "eSCN-L6-M3-Lay20-S2EF-OC20-All+MD"
+ # TODO: cannot assign device
+ local_cache="/tmp/ocp/",
+ cpu=False,
+ seed=0,
+ **kwargs,
+ ) -> None:
+ kwargs.pop("device", None)
+ super().__init__(
+ model_name=checkpoint,
+ local_cache=local_cache,
+ cpu=cpu,
+ seed=seed,
+ **kwargs,
+ )
+
+ def calculate(self, atoms: Atoms, properties, system_changes) -> None:
+ super().calculate(atoms, properties, system_changes)
+
+ self.results.update(
+ force=atoms.get_forces(),
+ )
diff --git a/mlip_arena/models/externals/fairchem.py b/mlip_arena/models/externals/fairchem.py
new file mode 100644
index 0000000000000000000000000000000000000000..403a8b4e5f2f7063c0a4b15fc7ae5b365bc2398b
--- /dev/null
+++ b/mlip_arena/models/externals/fairchem.py
@@ -0,0 +1,155 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import yaml
+from ase import Atoms
+from fairchem.core import OCPCalculator
+from huggingface_hub import hf_hub_download
+
+with open(Path(__file__).parents[1] / "registry.yaml", encoding="utf-8") as f:
+ REGISTRY = yaml.safe_load(f)
+
+
+class eSEN(OCPCalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["eSEN"]["checkpoint"],
+ cache_dir=None,
+ cpu=False, # TODO: cannot assign device
+ seed=0,
+ **kwargs,
+ ) -> None:
+
+ # https://huggingface.co/facebook/OMAT24/resolve/main/esen_30m_oam.pt
+
+ checkpoint_path = hf_hub_download(
+ "fairchem/OMAT24",
+ filename=checkpoint,
+ revision="13ab5b8d71af67bd1c83fbbf53250c82cd87f506",
+ cache_dir=cache_dir
+ )
+ kwargs.pop("device", None)
+ super().__init__(
+ checkpoint_path=checkpoint_path,
+ cpu=cpu,
+ seed=seed,
+ **kwargs,
+ )
+
+class eqV2(OCPCalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["eqV2(OMat)"]["checkpoint"],
+ cache_dir=None,
+ cpu=False, # TODO: cannot assign device
+ seed=0,
+ **kwargs,
+ ) -> None:
+ """
+ Initialize an eqV2 calculator.
+
+ Parameters
+ ----------
+ checkpoint : str, default="eqV2_86M_omat_mp_salex.pt"
+ The name of the eqV2 checkpoint to use.
+ local_cache : str, default="/tmp/ocp/"
+ The directory to store the downloaded checkpoint.
+ cpu : bool, default=False
+ Whether to run the model on CPU or GPU.
+ seed : int, default=0
+ The random seed for the model.
+
+ Other Parameters
+ ----------------
+ **kwargs
+ Any additional keyword arguments are passed to the superclass.
+ """
+
+ # https://huggingface.co/fairchem/OMAT24/resolve/main/eqV2_86M_omat_mp_salex.pt
+
+ checkpoint_path = hf_hub_download(
+ "fairchem/OMAT24",
+ filename=checkpoint,
+ revision="bf92f9671cb9d5b5c77ecb4aa8b317ff10b882ce",
+ cache_dir=cache_dir
+ )
+ kwargs.pop("device", None)
+ super().__init__(
+ checkpoint_path=checkpoint_path,
+ cpu=cpu,
+ seed=seed,
+ **kwargs,
+ )
+
+class EquiformerV2(OCPCalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["EquiformerV2(OC22)"]["checkpoint"],
+ # TODO: cannot assign device
+ local_cache="~/.cache/ocp/",
+ cpu=False,
+ seed=0,
+ **kwargs,
+ ) -> None:
+ kwargs.pop("device", None)
+ super().__init__(
+ model_name=checkpoint,
+ local_cache=local_cache,
+ cpu=cpu,
+ seed=seed,
+ **kwargs,
+ )
+
+ def calculate(self, atoms: Atoms, properties, system_changes) -> None:
+ super().calculate(atoms, properties, system_changes)
+
+ self.results.update(
+ force=atoms.get_forces(),
+ )
+
+
+class EquiformerV2OC20(OCPCalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["EquiformerV2(OC22)"]["checkpoint"],
+ # TODO: cannot assign device
+ local_cache="~/.cache/ocp/",
+ cpu=False,
+ seed=0,
+ **kwargs,
+ ) -> None:
+ kwargs.pop("device", None)
+ super().__init__(
+ model_name=checkpoint,
+ local_cache=local_cache,
+ cpu=cpu,
+ seed=seed,
+ **kwargs,
+ )
+
+class eSCN(OCPCalculator):
+ def __init__(
+ self,
+ checkpoint="eSCN-L6-M3-Lay20-S2EF-OC20-All+MD", # TODO: import from registry
+ # TODO: cannot assign device
+ local_cache="~/.cache/ocp/",
+ cpu=False,
+ seed=0,
+ **kwargs,
+ ) -> None:
+ kwargs.pop("device", None)
+ super().__init__(
+ model_name=checkpoint,
+ local_cache=local_cache,
+ cpu=cpu,
+ seed=seed,
+ **kwargs,
+ )
+
+ def calculate(self, atoms: Atoms, properties, system_changes) -> None:
+ super().calculate(atoms, properties, system_changes)
+
+ self.results.update(
+ force=atoms.get_forces(),
+ )
diff --git a/mlip_arena/models/externals/mace-mp.py b/mlip_arena/models/externals/mace-mp.py
new file mode 100644
index 0000000000000000000000000000000000000000..1015c7c4aadd329904cd49d28cffa8fabf1de777
--- /dev/null
+++ b/mlip_arena/models/externals/mace-mp.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+from mace.calculators import MACECalculator
+
+from mlip_arena.models.utils import get_freer_device
+
+
+class MACE_MP_Medium(MACECalculator):
+ def __init__(
+ self,
+ checkpoint="https://github.com/ACEsuit/mace-mp/releases/download/mace_mp_0/2023-12-03-mace-128-L1_epoch-199.model",
+ device: str | None = None,
+ default_dtype="float32",
+ **kwargs,
+ ):
+ cache_dir = Path.home() / ".cache" / "mace"
+ checkpoint_url_name = "".join(
+ c for c in os.path.basename(checkpoint) if c.isalnum() or c in "_"
+ )
+ cached_model_path = f"{cache_dir}/{checkpoint_url_name}"
+ if not os.path.isfile(cached_model_path):
+ import urllib
+
+ os.makedirs(cache_dir, exist_ok=True)
+ _, http_msg = urllib.request.urlretrieve(checkpoint, cached_model_path)
+ if "Content-Type: text/html" in http_msg:
+ raise RuntimeError(
+ f"Model download failed, please check the URL {checkpoint}"
+ )
+ model = cached_model_path
+
+ device = device or str(get_freer_device())
+
+ super().__init__(
+ model_paths=model, device=device, default_dtype=default_dtype, **kwargs
+ )
+
+class MACE_MPA(MACECalculator):
+ def __init__(
+ self,
+ checkpoint="https://github.com/ACEsuit/mace-mp/releases/download/mace_mpa_0/mace-mpa-0-medium.model",
+ device: str | None = None,
+ default_dtype="float32",
+ **kwargs,
+ ):
+ cache_dir = Path.home() / ".cache" / "mace"
+ checkpoint_url_name = "".join(
+ c for c in os.path.basename(checkpoint) if c.isalnum() or c in "_"
+ )
+ cached_model_path = f"{cache_dir}/{checkpoint_url_name}"
+ if not os.path.isfile(cached_model_path):
+ import urllib
+
+ os.makedirs(cache_dir, exist_ok=True)
+ _, http_msg = urllib.request.urlretrieve(checkpoint, cached_model_path)
+ if "Content-Type: text/html" in http_msg:
+ raise RuntimeError(
+ f"Model download failed, please check the URL {checkpoint}"
+ )
+ model = cached_model_path
+
+ device = device or str(get_freer_device())
+
+ super().__init__(
+ model_paths=model, device=device, default_dtype=default_dtype, **kwargs
+ )
\ No newline at end of file
diff --git a/mlip_arena/models/externals/mace-off.py b/mlip_arena/models/externals/mace-off.py
new file mode 100644
index 0000000000000000000000000000000000000000..712ddeb235834d1f25fcba8dac0f7a3a044d61aa
--- /dev/null
+++ b/mlip_arena/models/externals/mace-off.py
@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+from mace.calculators import MACECalculator
+
+from mlip_arena.models.utils import get_freer_device
+
+
+class MACE_OFF_Medium(MACECalculator):
+ def __init__(
+ self,
+ checkpoint="https://github.com/ACEsuit/mace-off/raw/main/mace_off23/MACE-OFF23_medium.model?raw=true",
+ device: str | None = None,
+ default_dtype="float32",
+ **kwargs,
+ ):
+ cache_dir = Path.home() / ".cache" / "mace"
+ checkpoint_url_name = "".join(
+ c for c in os.path.basename(checkpoint) if c.isalnum() or c in "_"
+ )
+ cached_model_path = f"{cache_dir}/{checkpoint_url_name}"
+ if not os.path.isfile(cached_model_path):
+ import urllib
+
+ os.makedirs(cache_dir, exist_ok=True)
+ _, http_msg = urllib.request.urlretrieve(checkpoint, cached_model_path)
+ if "Content-Type: text/html" in http_msg:
+ raise RuntimeError(
+ f"Model download failed, please check the URL {checkpoint}"
+ )
+ model = cached_model_path
+
+ device = device or str(get_freer_device())
+
+ super().__init__(
+ model_paths=model, device=device, default_dtype=default_dtype, **kwargs
+ )
\ No newline at end of file
diff --git a/mlip_arena/models/externals/matgl.py b/mlip_arena/models/externals/matgl.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c7d70ac3cb64e82197c1cbe9a1ff4c5f53ba556
--- /dev/null
+++ b/mlip_arena/models/externals/matgl.py
@@ -0,0 +1,21 @@
+from __future__ import annotations
+
+import matgl
+import torch
+from matgl.ext.ase import PESCalculator
+from typing import Literal
+
+
+class M3GNet(PESCalculator):
+ def __init__(
+ self,
+ checkpoint="M3GNet-MP-2021.2.8-PES",
+ # TODO: cannot assign device
+ state_attr: torch.Tensor | None = None,
+ stress_unit: Literal["eV/A3", "GPa"] = "GPa",
+ stress_weight: float = 1.0,
+ use_voigt: bool = False,
+ **kwargs,
+ ) -> None:
+ potential = matgl.load_model(checkpoint)
+ super().__init__(potential, state_attr, stress_unit, stress_weight, use_voigt, **kwargs)
diff --git a/mlip_arena/models/externals/mattersim.py b/mlip_arena/models/externals/mattersim.py
new file mode 100644
index 0000000000000000000000000000000000000000..28b78a23164720dc149b3b5a10fec854b9f47bc5
--- /dev/null
+++ b/mlip_arena/models/externals/mattersim.py
@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import yaml
+from mattersim.forcefield import MatterSimCalculator
+
+from mlip_arena.models.utils import get_freer_device
+
+with open(Path(__file__).parents[1] / "registry.yaml", encoding="utf-8") as f:
+ REGISTRY = yaml.safe_load(f)
+
+
+class MatterSim(MatterSimCalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["MatterSim"]["checkpoint"],
+ device=None,
+ **kwargs,
+ ):
+ super().__init__(
+ load_path=checkpoint, device=str(device or get_freer_device()), **kwargs
+ )
+
+ def get_potential_energy(self, atoms=None, force_consistent=False):
+ return float(
+ super().get_potential_energy(
+ atoms=atoms,
+ force_consistent=force_consistent,
+ )
+ ) # mattersim return numpy float instead of python float
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+
+ # BUG: remove unpicklizable potential
+ state.pop("potential", None)
+
+ return state
+
+ # def calculate(
+ # self,
+ # atoms: Atoms | None = None,
+ # properties: list | None = None,
+ # system_changes: list | None = None,
+ # ):
+ # super().calculate(atoms, properties, system_changes)
+
+ # # convert unpicklizable atoms back to picklizable atoms to avoid prefect pickling error
+ # if isinstance(self.atoms, MSONAtoms):
+ # atoms = self.atoms.copy()
+ # strucutre = AseAtomsAdaptor().get_structure(atoms)
+ # self.atoms = AseAtomsAdaptor().get_atoms(strucutre, msonable=False)
diff --git a/mlip_arena/models/externals/orb.py b/mlip_arena/models/externals/orb.py
new file mode 100644
index 0000000000000000000000000000000000000000..f02a00512aee9fa6b80479ef78e3f08ac99ab649
--- /dev/null
+++ b/mlip_arena/models/externals/orb.py
@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import yaml
+import requests
+from orb_models.forcefield import pretrained
+from orb_models.forcefield.calculator import ORBCalculator
+
+from mlip_arena.models.utils import get_freer_device
+
+with open(Path(__file__).parents[1] / "registry.yaml", encoding="utf-8") as f:
+ REGISTRY = yaml.safe_load(f)
+
+class ORB(ORBCalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["ORB"]["checkpoint"],
+ device=None,
+ **kwargs,
+ ):
+ device = device or get_freer_device()
+
+ cache_dir = Path.home() / ".cache" / "orb"
+ cache_dir.mkdir(parents=True, exist_ok=True)
+ ckpt_path = cache_dir / checkpoint
+
+ url = f"https://storage.googleapis.com/orbitalmaterials-public-models/forcefields/{checkpoint}"
+
+ if not ckpt_path.exists():
+ print(f"Downloading ORB model from {url} to {ckpt_path}...")
+ try:
+ response = requests.get(url, stream=True, timeout=120)
+ response.raise_for_status()
+ with open(ckpt_path, "wb") as f:
+ for chunk in response.iter_content(chunk_size=8192):
+ f.write(chunk)
+ print("Download completed.")
+ except requests.exceptions.RequestException as e:
+ raise RuntimeError("Failed to download ORB model.") from e
+
+ orbff = pretrained.orb_v1(weights_path=ckpt_path, device=device)
+ super().__init__(orbff, device=device, **kwargs)
+
+class ORBv2(ORBCalculator):
+ def __init__(
+ self,
+ checkpoint=REGISTRY["ORBv2"]["checkpoint"],
+ device=None,
+ **kwargs,
+ ):
+ device = device or get_freer_device()
+
+ cache_dir = Path.home() / ".cache" / "orb"
+ cache_dir.mkdir(parents=True, exist_ok=True)
+ ckpt_path = cache_dir / checkpoint
+
+ # url = f"https://storage.googleapis.com/orbitalmaterials-public-models/forcefields/{checkpoint}"
+ url = f"https://orbitalmaterials-public-models.s3.us-west-1.amazonaws.com/forcefields/{checkpoint}"
+
+ if not ckpt_path.exists():
+ print(f"Downloading ORB model from {url} to {ckpt_path}...")
+ try:
+ response = requests.get(url, stream=True, timeout=120)
+ response.raise_for_status()
+ with open(ckpt_path, "wb") as f:
+ for chunk in response.iter_content(chunk_size=8192):
+ f.write(chunk)
+ print("Download completed.")
+ except requests.exceptions.RequestException as e:
+ raise RuntimeError("Failed to download ORB model.") from e
+
+ orbff = pretrained.orb_v2(weights_path=ckpt_path, device=device)
+ super().__init__(orbff, device=device, **kwargs)
diff --git a/mlip_arena/models/externals/sevennet.py b/mlip_arena/models/externals/sevennet.py
new file mode 100644
index 0000000000000000000000000000000000000000..af8d13c86aaedf49e8e5c61e961d88b37db90ddf
--- /dev/null
+++ b/mlip_arena/models/externals/sevennet.py
@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+from sevenn.sevennet_calculator import SevenNetCalculator
+
+from mlip_arena.models.utils import get_freer_device
+
+
+class SevenNet(SevenNetCalculator):
+ def __init__(
+ self,
+ checkpoint="7net-0", # TODO: import from registry
+ device=None,
+ **kwargs,
+ ):
+ device = device or get_freer_device()
+ super().__init__(checkpoint, device=device, **kwargs)
diff --git a/mlip_arena/models/mace.py b/mlip_arena/models/mace.py
new file mode 100644
index 0000000000000000000000000000000000000000..382e80a73a33e97e3afefb1d9030bfdcb68facb7
--- /dev/null
+++ b/mlip_arena/models/mace.py
@@ -0,0 +1,58 @@
+import torch
+from ase import Atoms
+from ase.calculators.calculator import all_changes
+from huggingface_hub import hf_hub_download
+from torch_geometric.data import Data
+
+from mlip_arena.models import MLIPCalculator
+
+
+class MACE_MP_Medium(MLIPCalculator):
+ def __init__(
+ self,
+ device: torch.device | None = None,
+ restart=None,
+ atoms=None,
+ directory=".",
+ **kwargs,
+ ):
+ self.device = device or torch.device(
+ "cuda" if torch.cuda.is_available() else "cpu"
+ )
+
+ fpath = hf_hub_download(
+ repo_id="cyrusyc/mace-universal",
+ subfolder="pretrained",
+ filename="2023-12-12-mace-128-L1_epoch-199.model",
+ revision="main",
+ )
+
+ model = torch.load(fpath, map_location=self.device)
+
+ super().__init__(
+ model=model, restart=restart, atoms=atoms, directory=directory, **kwargs
+ )
+
+ self.name: str = self.__class__.__name__
+ self.implemented_properties = ["energy", "forces", "stress"]
+
+ def calculate(
+ self, atoms: Atoms, properties: list[str], system_changes: list = all_changes
+ ):
+ """Calculate energies and forces for the given Atoms object"""
+ super().calculate(atoms, properties, system_changes)
+
+ output = self.forward(atoms)
+
+ self.results = {}
+ if "energy" in properties:
+ self.results["energy"] = output["energy"].item()
+ if "forces" in properties:
+ self.results["forces"] = output["forces"].cpu().detach().numpy()
+ if "stress" in properties:
+ self.results["stress"] = output["stress"].cpu().detach().numpy()
+
+ def forward(self, x: Data | Atoms) -> dict[str, torch.Tensor]:
+ """Implement data conversion, graph creation, and model forward pass"""
+ # TODO
+ raise NotImplementedError
diff --git a/mlip_arena/models/registry.yaml b/mlip_arena/models/registry.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..50d70407e273bd6a096604db5a36ed7f5189988b
--- /dev/null
+++ b/mlip_arena/models/registry.yaml
@@ -0,0 +1,407 @@
+MACE-MP(M):
+ module: externals
+ class: MACE_MP_Medium
+ family: mace-mp
+ package: mace-torch==0.3.9
+ checkpoint: 2023-12-03-mace-128-L1_epoch-199.model
+ username: cyrusyc
+ last-update: 2024-03-25T14:30:00
+ datetime: 2024-03-25T14:30:00 # TODO: Fake datetime
+ datasets:
+ - MPTrj # TODO: fake HF dataset repo
+ cpu-tasks:
+ - eos_alloy
+ gpu-tasks:
+ - homonuclear-diatomics
+ - stability
+ - combustion
+ - eos_bulk
+ - wbm_ev
+ github: https://github.com/ACEsuit/mace
+ doi: https://arxiv.org/abs/2401.00096
+ date: 2023-12-29
+ prediction: EFS
+ nvt: true
+ npt: true
+ license: MIT
+
+CHGNet:
+ module: externals
+ class: CHGNet
+ family: chgnet
+ package: chgnet==0.3.8
+ checkpoint: v0.3.0
+ username: cyrusyc
+ last-update: 2024-07-08T00:00:00
+ datetime: 2024-07-08T00:00:00
+ datasets:
+ - MPTrj
+ gpu-tasks:
+ - homonuclear-diatomics
+ - stability
+ - combustion
+ - eos_bulk
+ - wbm_ev
+ github: https://github.com/CederGroupHub/chgnet
+ doi: https://doi.org/10.1038/s42256-023-00716-3
+ date: 2023-02-28
+ prediction: EFSM
+ nvt: true
+ npt: true
+ license: BSD-3-Clause
+
+M3GNet:
+ module: externals
+ class: M3GNet
+ family: matgl
+ package: matgl==1.1.2
+ checkpoint: M3GNet-MP-2021.2.8-PES
+ username: cyrusyc
+ last-update: 2024-07-08T00:00:00
+ datetime: 2024-07-08T00:00:00
+ datasets:
+ - MPF
+ gpu-tasks:
+ - homonuclear-diatomics
+ - combustion
+ - stability
+ - eos_bulk
+ - wbm_ev
+ github: https://github.com/materialsvirtuallab/matgl
+ doi: https://doi.org/10.1038/s43588-022-00349-3
+ date: 2022-02-05
+ prediction: EFS
+ nvt: true
+ npt: true
+ license: BSD-3-Clause
+
+MatterSim:
+ module: externals
+ class: MatterSim
+ family: mattersim
+ package: mattersim==1.0.0rc9
+ checkpoint: MatterSim-v1.0.0-5M.pth
+ username:
+ last-update: 2024-10-29T00:00:00
+ datetime: 2024-10-29T00:00:00 # TODO: Fake datetime
+ datasets:
+ - MPTrj
+ - Alexandria
+ cpu-tasks:
+ - eos_alloy
+ gpu-tasks:
+ - homonuclear-diatomics
+ - stability
+ - combustion
+ - eos_bulk
+ - wbm_ev
+ github: https://github.com/microsoft/mattersim
+ doi: https://arxiv.org/abs/2405.04967
+ date: 2024-12-05
+ prediction: EFS
+ nvt: true
+ npt: true
+ license: MIT
+
+ORBv2:
+ module: externals
+ class: ORBv2
+ family: orb
+ package: orb-models==0.4.0
+ checkpoint: orb-v2-20241011.ckpt
+ username:
+ last-update: 2024-10-29T00:00:00
+ datetime: 2024-10-29T00:00:00 # TODO: Fake datetime
+ datasets:
+ - MPTrj
+ - Alexandria
+ cpu-tasks:
+ - eos_alloy
+ gpu-tasks:
+ - homonuclear-diatomics
+ - combustion
+ - stability
+ - eos_bulk
+ - wbm_ev
+ github: https://github.com/orbital-materials/orb-models
+ doi: https://arxiv.org/abs/2410.22570
+ date: 2024-10-15
+ prediction: EFS
+ nvt: true
+ npt: true
+ license: Apache-2.0
+
+SevenNet:
+ module: externals
+ class: SevenNet
+ family: sevennet
+ package: sevenn==0.9.4
+ checkpoint: 7net-0
+ username: cyrusyc
+ last-update: 2024-03-25T14:30:00
+ datetime: 2024-03-25T14:30:00 # TODO: Fake datetime
+ datasets:
+ - MPTrj # TODO: fake HF dataset repo
+ gpu-tasks:
+ - homonuclear-diatomics
+ - stability
+ - combustion
+ - eos_bulk
+ - wbm_ev
+ github: https://github.com/MDIL-SNU/SevenNet
+ doi: https://doi.org/10.1021/acs.jctc.4c00190
+ date: 2024-07-11
+ prediction: EFS
+ nvt: true
+ npt: true
+ license: GPL-3.0-only
+
+eqV2(OMat):
+ module: externals
+ class: eqV2
+ family: fairchem
+ package: fairchem-core==1.2.0
+ checkpoint: eqV2_86M_omat_mp_salex.pt
+ username: fairchem # HF handle
+ last-update: 2024-10-18T00:00:00
+ datetime: 2024-10-18T00:00:00
+ datasets:
+ - OMat
+ - MPTrj
+ - Alexandria
+ cpu-tasks:
+ - eos_alloy
+ gpu-tasks:
+ - homonuclear-diatomics
+ - wbm_ev
+ prediction: EFS
+ nvt: true
+ npt: false # https://github.com/FAIR-Chem/fairchem/issues/888, https://github.com/atomind-ai/mlip-arena/issues/17
+ date: 2024-10-18
+ github: https://github.com/FAIR-Chem/fairchem
+ doi: https://arxiv.org/abs/2410.12771
+ license: Modified Apache-2.0 (Meta)
+
+MACE-MPA:
+ module: externals
+ class: MACE_MPA
+ family: mace-mp
+ package: mace-torch==0.3.9
+ checkpoint: mace-mpa-0-medium.model
+ username:
+ last-update: 2025-11-19T00:00:00
+ datetime: 2024-12-09T00:00:00 # TODO: Fake datetime
+ datasets:
+ - MPTrj # TODO: fake HF dataset repo
+ - Alexandria
+ gpu-tasks:
+ - homonuclear-diatomics
+ - stability
+ - eos_bulk
+ - wbm_ev
+ github: https://github.com/ACEsuit/mace
+ doi: https://arxiv.org/abs/2401.00096
+ date: 2024-12-09
+ prediction: EFS
+ nvt: true
+ npt: true
+ license: MIT
+
+eSEN:
+ module: externals
+ class: eSEN
+ family: fairchem
+ package: fairchem-core==1.10.0
+ checkpoint: esen_30m_oam.pt
+ username: fairchem # HF handle
+ last-update: 2025-04-21
+ datetime: 2025-04-21
+ datasets:
+ - OMat
+ - MPTrj
+ - Alexandria
+ gpu-tasks:
+ - homonuclear-diatomics
+ - wbm_ev
+ - eos_bulk
+ prediction: EFS
+ nvt: true
+ npt: true
+ date: 2025-04-14
+ github: https://github.com/FAIR-Chem/fairchem
+ doi: https://arxiv.org/abs/2502.12147
+ license: Modified Apache-2.0 (Meta)
+
+EquiformerV2(OC22):
+ module: externals
+ class: EquiformerV2
+ family: equiformer
+ package: fairchem-core==1.2.0
+ checkpoint: EquiformerV2-lE4-lF100-S2EFS-OC22
+ username: cyrusyc
+ last-update: 2024-07-08T00:00:00
+ datetime: 2024-07-08T00:00:00
+ datasets:
+ - OC22
+ gpu-tasks:
+ - homonuclear-diatomics
+ - combustion
+ github: https://github.com/FAIR-Chem/fairchem
+ doi: https://arxiv.org/abs/2306.12059
+ date: 2023-06-21
+ prediction: EF
+ nvt: true
+ npt: false
+ license:
+
+EquiformerV2(OC20):
+ module: externals
+ class: EquiformerV2OC20
+ family: equiformer
+ package: fairchem-core==1.2.0
+ checkpoint: EquiformerV2-31M-S2EF-OC20-All+MD
+ username: cyrusyc
+ last-update: 2024-07-08T00:00:00
+ datetime: 2024-07-08T00:00:00
+ datasets:
+ - OC20
+ gpu-tasks:
+ - homonuclear-diatomics
+ - combustion
+ github: https://github.com/FAIR-Chem/fairchem
+ doi: https://arxiv.org/abs/2306.12059
+ date: 2023-06-21
+ prediction: EF
+ nvt: true
+ npt: false
+
+eSCN(OC20):
+ module: externals
+ class: eSCN
+ family: escn
+ package: fairchem-core==1.2.0
+ checkpoint: eSCN-L6-M3-Lay20-S2EF-OC20-All+MD
+ username: cyrusyc
+ last-update: 2024-07-08T00:00:00
+ datetime: 2024-07-08T00:00:00
+ datasets:
+ - OC20
+ gpu-tasks:
+ - homonuclear-diatomics
+ - combustion
+ github: https://github.com/FAIR-Chem/fairchem
+ doi: https://arxiv.org/abs/2302.03655
+ date: 2023-02-07
+ prediction: EF
+ nvt: true
+ npt: false
+ license:
+
+MACE-OFF(M):
+ module: externals
+ class: MACE_OFF_Medium
+ family: mace-off
+ package: mace-torch==0.3.9
+ checkpoint: MACE-OFF23_medium.model
+ username: cyrusyc
+ last-update: 2024-03-25T14:30:00
+ datetime: 2024-03-25T14:30:00 # TODO: Fake datetime
+ datasets:
+ - SPICE # TODO: fake HF dataset repo
+ gpu-tasks:
+ - homonuclear-diatomics
+ github: https://github.com/ACEsuit/mace
+ doi: https://arxiv.org/abs/2312.15211
+ date: 2023-12-23
+ prediction: EFS
+ nvt: true
+ npt: true
+ license: ASL
+
+ANI2x:
+ module: externals
+ class: ANI2x
+ family: ani
+ package: torchani==2.2.4
+ checkpoint: ani-2x_8x.info
+ username: cyrusyc
+ last-update: 2024-12-11T16:00:00
+ datetime: 2024-12-11T16:00:00 # TODO: Fake datetime
+ datasets:
+ cpu-tasks:
+ gpu-tasks:
+ - homonuclear-diatomics
+ github: https://github.com/aiqm/torchani
+ doi: https://www.nature.com/articles/s41598-024-62242-5
+ date: 2024-05-23
+ prediction: EFS
+ nvt: true
+ npt: true
+ license: MIT
+
+ALIGNN:
+ module: externals
+ class: ALIGNN
+ family: alignn
+ package: alignn==2024.5.27
+ checkpoint: 2024.5.27
+ username: cyrusyc
+ last-update: 2024-07-08T00:00:00
+ datetime: 2024-07-08T00:00:00
+ datasets:
+ - MP22
+ gpu-tasks:
+ - homonuclear-diatomics
+ - stability
+ - wbm_ev
+ # - combustion
+ prediction: EFS
+ nvt: true
+ npt: true
+ github: https://github.com/usnistgov/alignn
+ doi: https://doi.org/10.1038/s41524-021-00650-1
+ date: 2021-11-15
+ license:
+
+DeepMD:
+ module: externals
+ class: DeepMD
+ family: deepmd
+ package: deepmd-kit==v3.0.0b4
+ checkpoint: dp0808c_v024mixu.pth
+ username:
+ last-update: 2024-10-09T00:00:00
+ datetime: 2024-03-25T14:30:00 # TODO: Fake datetime
+ datasets:
+ - MPTrj # TODO: fake HF dataset repo
+ github: https://github.com/deepmodeling/deepmd-kit/
+ doi: https://arxiv.org/abs/2312.15492
+ date: 2024-10-09
+ prediction: EFS
+ nvt: true
+ npt: true
+ license:
+
+ORB:
+ module: externals
+ class: ORB
+ family: orb
+ package: orb-models==0.3.1
+ checkpoint: orbff-v1-20240827.ckpt
+ username: cyrusyc
+ last-update: 2024-03-25T14:30:00
+ datetime: 2024-03-25T14:30:00 # TODO: Fake datetime
+ datasets:
+ - MPTrj # TODO: fake HF dataset repo
+ - Alexandria
+ gpu-tasks:
+ - homonuclear-diatomics
+ - combustion
+ - stability
+ github: https://github.com/orbital-materials/orb-models
+ doi:
+ date: 2024-09-03
+ prediction: EFS
+ nvt: true
+ npt: true
+ license: Apache-2.0
\ No newline at end of file
diff --git a/mlip_arena/models/utils.py b/mlip_arena/models/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..40d7aa9085e3d90b698ceb78801e9dea9c83a384
--- /dev/null
+++ b/mlip_arena/models/utils.py
@@ -0,0 +1,44 @@
+"""Utility functions for MLIP models."""
+
+import torch
+
+try:
+ from prefect.logging import get_run_logger
+
+ logger = get_run_logger()
+except (ImportError, RuntimeError):
+ from loguru import logger
+
+
+def get_freer_device() -> torch.device:
+ """Get the GPU with the most free memory, or use MPS if available.
+
+ Returns:
+ torch.device: The selected GPU device or MPS.
+
+ Raises:
+ ValueError: If no GPU or MPS is available.
+ """
+ device_count = torch.cuda.device_count()
+ if device_count > 0:
+ # If CUDA GPUs are available, select the one with the most free memory
+ mem_free = [
+ torch.cuda.get_device_properties(i).total_memory
+ - torch.cuda.memory_allocated(i)
+ for i in range(device_count)
+ ]
+ free_gpu_index = mem_free.index(max(mem_free))
+ device = torch.device(f"cuda:{free_gpu_index}")
+ logger.info(
+ f"Selected GPU {device} with {mem_free[free_gpu_index] / 1024**2:.2f} MB free memory from {device_count} GPUs"
+ )
+ elif torch.backends.mps.is_available():
+ # If no CUDA GPUs are available but MPS is, use MPS
+ logger.info("No GPU available. Using MPS.")
+ device = torch.device("mps")
+ else:
+ # Fallback to CPU if neither CUDA GPUs nor MPS are available
+ logger.info("No GPU or MPS available. Using CPU.")
+ device = torch.device("cpu")
+
+ return device
diff --git a/mlip_arena/tasks/README.md b/mlip_arena/tasks/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b2be171a46e3596709631a323bef18f53c61069d
--- /dev/null
+++ b/mlip_arena/tasks/README.md
@@ -0,0 +1,26 @@
+
+
+## Task
+
+In the language of Prefect workflow manager, we define a task as *one operation on one input structure* that generates result for **one sample**. For example, [Structure optimization (OPT)](optimize.py) initiates one structure optimization on one structure and return the relaxed structure.
+
+It is possible to chain multiple subtasks into a single, complex task. For example, [Equation of states (EOS)](eos.py) first performs one full relaxed [OPT](optimize.py) task and parallelizes/serializes multiple constrained [OPT](optimize.py) tasks in one call, and returns the equation of state and bulk modulus of the structure.
+
+There are some general tasks that can be reused:
+- [Structure optimization (OPT)](optimize.py)
+- [Molecular dynamics (MD)](md.py)
+- [Equation of states (EOS)](eos.py)
+
+## Flow
+
+Flow is meant to be used to parallize multiple tasks and be orchestrated for production at scale on high-throughput cluster.
+
+
+
\ No newline at end of file
diff --git a/mlip_arena/tasks/__init__.py b/mlip_arena/tasks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7c0819d3f6a00477b24be3f9855c99715052e5c
--- /dev/null
+++ b/mlip_arena/tasks/__init__.py
@@ -0,0 +1,73 @@
+from pathlib import Path
+
+import yaml
+from huggingface_hub import HfApi, HfFileSystem, hf_hub_download
+
+# from mlip_arena.models import MLIP
+# from mlip_arena.models import REGISTRY as MODEL_REGISTRY
+
+try:
+ from prefect.logging import get_run_logger
+
+ logger = get_run_logger()
+except (ImportError, RuntimeError):
+ from loguru import logger
+
+try:
+ from .elasticity import run as ELASTICITY
+ from .eos import run as EOS
+ from .md import run as MD
+ from .neb import run as NEB
+ from .neb import run_from_endpoints as NEB_FROM_ENDPOINTS
+ from .optimize import run as OPT
+ from .phonon import run as PHONON
+
+ __all__ = ["OPT", "EOS", "MD", "NEB", "NEB_FROM_ENDPOINTS", "ELASTICITY", "PHONON"]
+except (ImportError, TypeError, NameError) as e:
+ logger.warning(e)
+
+
+with open(Path(__file__).parent / "registry.yaml", encoding="utf-8") as f:
+ REGISTRY = yaml.safe_load(f)
+
+
+# class Task:
+# def __init__(self):
+# self.name: str = self.__class__.__name__ # display name on the leaderboard
+
+# def run_local(self, model: MLIP):
+# """Run the task using the given model and return the results."""
+# raise NotImplementedError
+
+# def run_hf(self, model: MLIP):
+# """Run the task using the given model and return the results."""
+# raise NotImplementedError
+
+# # Calcualte evaluation metrics and postprocessed data
+# api = HfApi()
+# api.upload_file(
+# path_or_fileobj="results.json",
+# path_in_repo=f"{self.__class__.__name__}/{model.__class__.__name__}/results.json", # Upload to a specific folder
+# repo_id="atomind/mlip-arena",
+# repo_type="dataset",
+# )
+
+# def run_nersc(self, model: MLIP):
+# """Run the task using the given model and return the results."""
+# raise NotImplementedError
+
+# def get_results(self):
+# """Get the results from the task."""
+# # fs = HfFileSystem()
+# # files = fs.glob(f"datasets/atomind/mlip-arena/{self.__class__.__name__}/*/*.json")
+
+# for model, metadata in MODEL_REGISTRY.items():
+# results = hf_hub_download(
+# repo_id="atomind/mlip-arena",
+# filename="results.json",
+# subfolder=f"{self.__class__.__name__}/{model}",
+# repo_type="dataset",
+# revision=None,
+# )
+
+# return results
diff --git a/mlip_arena/tasks/combustion/H256O128.extxyz b/mlip_arena/tasks/combustion/H256O128.extxyz
new file mode 100644
index 0000000000000000000000000000000000000000..59fcd94cbba957e18f9626b2755866d01dfaac1b
--- /dev/null
+++ b/mlip_arena/tasks/combustion/H256O128.extxyz
@@ -0,0 +1,386 @@
+384
+Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3 Built=T with=T Packmol=T pbc="T T T"
+H 16.87897000 14.36340900 5.78639500
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diff --git a/mlip_arena/tasks/combustion/alignn/hydrogen.json b/mlip_arena/tasks/combustion/alignn/hydrogen.json
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--- /dev/null
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+version https://git-lfs.github.com/spec/v1
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diff --git a/mlip_arena/tasks/combustion/chgnet/hydrogen.json b/mlip_arena/tasks/combustion/chgnet/hydrogen.json
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diff --git a/mlip_arena/tasks/combustion/equiformer/hydrogen.json b/mlip_arena/tasks/combustion/equiformer/hydrogen.json
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diff --git a/mlip_arena/tasks/combustion/escn/hydrogen.json b/mlip_arena/tasks/combustion/escn/hydrogen.json
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diff --git a/mlip_arena/tasks/combustion/mace-mp/hydrogen.json b/mlip_arena/tasks/combustion/mace-mp/hydrogen.json
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diff --git a/mlip_arena/tasks/combustion/matgl/hydrogen.json b/mlip_arena/tasks/combustion/matgl/hydrogen.json
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diff --git a/mlip_arena/tasks/combustion/water.ipynb b/mlip_arena/tasks/combustion/water.ipynb
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index 0000000000000000000000000000000000000000..18ea30955cac7a96cb7caab2c9415a74641109ae
--- /dev/null
+++ b/mlip_arena/tasks/combustion/water.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "from dask.distributed import Client\n",
+ "from dask_jobqueue import SLURMCluster\n",
+ "from mlip_arena.models import REGISTRY, MLIPEnum\n",
+ "from mlip_arena.tasks.md import run as MD\n",
+ "from mlip_arena.tasks.utils import get_calculator\n",
+ "from prefect import flow\n",
+ "from prefect_dask import DaskTaskRunner\n",
+ "\n",
+ "from ase import Atoms, units\n",
+ "from ase.build import molecule\n",
+ "from ase.io import read, write\n",
+ "from pymatgen.core import Molecule\n",
+ "from pymatgen.io.packmol import PackmolBoxGen"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "## Intial configuration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "h2 = molecule(\"H2\")\n",
+ "o2 = molecule(\"O2\")\n",
+ "h2o = molecule(\"H2O\")\n",
+ "\n",
+ "write(\"h2.xyz\", h2)\n",
+ "write(\"o2.xyz\", o2)\n",
+ "write(\"h2o.xyz\", h2o)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "h2 = Molecule.from_file(\"h2.xyz\")\n",
+ "o2 = Molecule.from_file(\"o2.xyz\")\n",
+ "h2o = Molecule.from_file(\"h2o.xyz\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "molecules = []\n",
+ "\n",
+ "for m, number in zip([h2, o2], [128, 64]):\n",
+ " molecules.append(\n",
+ " {\n",
+ " \"name\": m.composition.to_pretty_string(),\n",
+ " \"number\": number,\n",
+ " \"coords\": m,\n",
+ " }\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tolerance = 2.0\n",
+ "input_gen = PackmolBoxGen(\n",
+ " tolerance=tolerance,\n",
+ " seed=1,\n",
+ ")\n",
+ "margin = 0.5 * tolerance\n",
+ "\n",
+ "a = 30\n",
+ "\n",
+ "packmol_set = input_gen.get_input_set(\n",
+ " molecules=molecules,\n",
+ " box=[margin, margin, margin, a - margin, a - margin, a - margin],\n",
+ ")\n",
+ "packmol_set.write_input(\".\")\n",
+ "packmol_set.run(\".\")\n",
+ "\n",
+ "atoms = read(\"packmol_out.xyz\")\n",
+ "atoms.cell = [a, a, a]\n",
+ "atoms.pbc = True\n",
+ "\n",
+ "print(atoms)\n",
+ "\n",
+ "write(f\"{atoms.get_chemical_formula()}.extxyz\", atoms)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Run workflow"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "atoms = read(\"H256O128.extxyz\")\n",
+ "print(atoms)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nodes_per_alloc = 1\n",
+ "gpus_per_alloc = 4\n",
+ "ntasks = 1\n",
+ "\n",
+ "cluster_kwargs = dict(\n",
+ " cores=1,\n",
+ " memory=\"64 GB\",\n",
+ " shebang=\"#!/bin/bash\",\n",
+ " account=\"m4282\",\n",
+ " walltime=\"00:30:00\",\n",
+ " job_mem=\"0\",\n",
+ " job_script_prologue=[\n",
+ " \"source ~/.bashrc\",\n",
+ " \"module load python\",\n",
+ " \"source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\",\n",
+ " ],\n",
+ " job_directives_skip=[\"-n\", \"--cpus-per-task\", \"-J\"],\n",
+ " job_extra_directives=[\n",
+ " \"-J combustion-water\",\n",
+ " \"-q debug\",\n",
+ " f\"-N {nodes_per_alloc}\",\n",
+ " \"-C gpu\",\n",
+ " f\"-G {gpus_per_alloc}\",\n",
+ " \"--exclusive\",\n",
+ " ],\n",
+ " death_timeout=86400,\n",
+ ")\n",
+ "\n",
+ "cluster = SLURMCluster(**cluster_kwargs)\n",
+ "\n",
+ "\n",
+ "print(cluster.job_script())\n",
+ "cluster.adapt(minimum_jobs=1, maximum_jobs=1)\n",
+ "client = Client(cluster)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@flow(task_runner=DaskTaskRunner(address=client.scheduler.address), log_prints=True)\n",
+ "def combustion(atoms: Atoms):\n",
+ " futures = []\n",
+ "\n",
+ " for model in MLIPEnum:\n",
+ " if model.name != \"MatterSim\":\n",
+ " continue\n",
+ "\n",
+ " future = MD.submit(\n",
+ " atoms=atoms,\n",
+ " calculator=get_calculator(\n",
+ " calculator_name=model,\n",
+ " calculator_kwargs=None,\n",
+ " ),\n",
+ " ensemble=\"nvt\",\n",
+ " dynamics=\"nose-hoover\",\n",
+ " time_step=None,\n",
+ " dynamics_kwargs=dict(ttime=25 * units.fs, pfactor=None),\n",
+ " total_time=1000_000,\n",
+ " temperature=[300, 3000, 3000, 300],\n",
+ " pressure=None,\n",
+ " velocity_seed=0,\n",
+ " traj_file=Path(REGISTRY[model.name][\"family\"])\n",
+ " / f\"{model.name}_{atoms.get_chemical_formula()}.traj\",\n",
+ " traj_interval=1000,\n",
+ " restart=True,\n",
+ " )\n",
+ "\n",
+ " futures.append(future)\n",
+ "\n",
+ " return [future.result() for future in futures]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "results = combustion(atoms)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def combustion(atoms: Atoms):\n",
+ " futures = []\n",
+ "\n",
+ " for model in MLIPEnum:\n",
+ " if model.name != \"MatterSim\":\n",
+ " continue\n",
+ "\n",
+ " future = MD(\n",
+ " atoms=atoms,\n",
+ " calculator=get_calculator(\n",
+ " calculator_name=model,\n",
+ " calculator_kwargs=None,\n",
+ " ),\n",
+ " ensemble=\"nvt\",\n",
+ " dynamics=\"nose-hoover\",\n",
+ " time_step=None,\n",
+ " dynamics_kwargs=dict(ttime=25 * units.fs, pfactor=None),\n",
+ " total_time=1000_000,\n",
+ " temperature=[300, 3000, 3000, 300],\n",
+ " pressure=None,\n",
+ " velocity_seed=0,\n",
+ " traj_file=Path(REGISTRY[model.name][\"family\"])\n",
+ " / f\"{model.name}_{atoms.get_chemical_formula()}.traj\",\n",
+ " traj_interval=1000,\n",
+ " restart=True,\n",
+ " )\n",
+ "\n",
+ " futures.append(future)\n",
+ "\n",
+ " return [future.result() for future in futures]\n",
+ "\n",
+ "\n",
+ "results = combustion(atoms)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "mlip-arena",
+ "language": "python",
+ "name": "mlip-arena"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "state": {},
+ "version_major": 2,
+ "version_minor": 0
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/mlip_arena/tasks/diatomics/__init__.py b/mlip_arena/tasks/diatomics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mlip_arena/tasks/diatomics/alignn/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/alignn/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..dbb99fb1da19c5ceeb2b4de01587b239950cfcb4
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/alignn/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:325979c740279016c3652f570b742d0ed28065156e553ae6c878a775951c344b
+size 2383784
diff --git a/mlip_arena/tasks/diatomics/analysis.py b/mlip_arena/tasks/diatomics/analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2f84ff9302cec37b8e92c6feede0038f93c66c9
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/analysis.py
@@ -0,0 +1,198 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from ase.data import chemical_symbols
+from ase.io import read
+from scipy import stats
+from scipy.interpolate import UnivariateSpline
+from tqdm.auto import tqdm
+
+from mlip_arena.models import REGISTRY, MLIPEnum
+
+for model in MLIPEnum:
+
+ df = pd.DataFrame(
+ columns=[
+ "name",
+ "method",
+ "R",
+ "E",
+ "F",
+ "S^2",
+ "force-flip-times",
+ "force-total-variation",
+ "force-jump",
+ "energy-diff-flip-times",
+ "energy-grad-norm-max",
+ "energy-jump",
+ "energy-total-variation",
+ "tortuosity",
+ "conservation-deviation",
+ "spearman-descending-force",
+ "spearman-ascending-force",
+ "spearman-repulsion-energy",
+ "spearman-attraction-energy",
+ "pbe-energy-mae",
+ "pbe-force-mae",
+ ]
+ )
+
+ for symbol in tqdm(chemical_symbols[1:]):
+ da = symbol + symbol
+
+ out_dir = Path(model.name)
+
+ traj_fpath = out_dir / f"{str(da)}.extxyz"
+
+ if traj_fpath.exists():
+ traj = read(traj_fpath, index=":")
+ else:
+ continue
+
+ Rs, Es, Fs, S2s = [], [], [], []
+ for atoms in traj:
+ vec = atoms.positions[1] - atoms.positions[0]
+ r = np.linalg.norm(vec)
+ e = atoms.get_potential_energy()
+ f = np.inner(vec / r, atoms.get_forces()[1])
+ # s2 = np.mean(np.power(atoms.get_magnetic_moments(), 2))
+
+ Rs.append(r)
+ Es.append(e)
+ Fs.append(f)
+ # S2s.append(s2)
+
+ rs = np.array(Rs)
+ es = np.array(Es)
+ fs = np.array(Fs)
+
+ # sort interatomic distances and align to zero at far field
+ indices = np.argsort(rs)[::-1]
+ rs = rs[indices]
+ es = es[indices]
+ eshift = es[0]
+ es -= eshift
+ fs = fs[indices]
+
+ iminf = np.argmin(fs)
+ imine = np.argmin(es)
+
+ de_dr = np.gradient(es, rs)
+ d2e_dr2 = np.gradient(de_dr, rs)
+
+ # avoid numerical sensitity close to zero
+ rounded_fs = np.copy(fs)
+ rounded_fs[np.abs(rounded_fs) < 1e-2] = 0 # 10meV/A
+ fs_sign = np.sign(rounded_fs)
+ mask = fs_sign != 0
+ rounded_fs = rounded_fs[mask]
+ fs_sign = fs_sign[mask]
+ f_flip = np.diff(fs_sign) != 0
+
+ fdiff = np.diff(fs)
+ fdiff_sign = np.sign(fdiff)
+ mask = fdiff_sign != 0
+ fdiff = fdiff[mask]
+ fdiff_sign = fdiff_sign[mask]
+ fdiff_flip = np.diff(fdiff_sign) != 0
+ fjump = (
+ np.abs(fdiff[:-1][fdiff_flip]).sum() + np.abs(fdiff[1:][fdiff_flip]).sum()
+ )
+
+ ediff = np.diff(es)
+ ediff[np.abs(ediff) < 1e-3] = 0 # 1meV
+ ediff_sign = np.sign(ediff)
+ mask = ediff_sign != 0
+ ediff = ediff[mask]
+ ediff_sign = ediff_sign[mask]
+ ediff_flip = np.diff(ediff_sign) != 0
+ ejump = (
+ np.abs(ediff[:-1][ediff_flip]).sum() + np.abs(ediff[1:][ediff_flip]).sum()
+ )
+
+ try:
+ pbe_traj = read(f"./vasp/{da}/PBE.extxyz", index=":")
+
+ pbe_rs, pbe_es, pbe_fs = [], [], []
+
+ for atoms in pbe_traj:
+ vec = atoms.positions[1] - atoms.positions[0]
+ r = np.linalg.norm(vec)
+ pbe_rs.append(r)
+ pbe_es.append(atoms.get_potential_energy())
+ pbe_fs.append(np.inner(vec / r, atoms.get_forces()[1]))
+
+ pbe_rs = np.array(pbe_rs)
+ pbe_es = np.array(pbe_es)
+ pbe_fs = np.array(pbe_fs)
+
+ indices = np.argsort(pbe_rs)
+ pbe_rs = pbe_rs[indices]
+ pbe_es = pbe_es[indices]
+ pbe_fs = pbe_fs[indices]
+
+ pbe_es -= pbe_es[-1]
+
+ xs = np.linspace(pbe_rs.min(), pbe_rs.max(), int(1e3))
+
+ cs = UnivariateSpline(pbe_rs, pbe_es, s=0)
+ pbe_energy_mae = np.mean(np.abs(es - cs(rs)))
+
+ cs = UnivariateSpline(pbe_rs, pbe_fs, s=0)
+ pbe_force_mae = np.mean(np.abs(fs - cs(rs)))
+ except Exception as e:
+ print(e)
+ pbe_energy_mae = None
+ pbe_force_mae = None
+
+ conservation_deviation = np.mean(np.abs(fs + de_dr))
+
+ etv = np.sum(np.abs(np.diff(es)))
+
+ data = {
+ "name": da,
+ "method": model.name,
+ "R": rs,
+ "E": es + eshift,
+ "F": fs,
+ "S^2": S2s,
+ "force-flip-times": np.sum(f_flip),
+ "force-total-variation": np.sum(np.abs(np.diff(fs))),
+ "force-jump": fjump,
+ "energy-diff-flip-times": np.sum(ediff_flip),
+ "energy-grad-norm-max": np.max(np.abs(de_dr)),
+ "energy-jump": ejump,
+ # "energy-grad-norm-mean": np.mean(de_dr_abs),
+ "energy-total-variation": etv,
+ "tortuosity": etv / (abs(es[0] - es.min()) + (es[-1] - es.min())),
+ "conservation-deviation": conservation_deviation,
+ "spearman-descending-force": stats.spearmanr(
+ rs[iminf:], fs[iminf:]
+ ).statistic,
+ "spearman-ascending-force": stats.spearmanr(
+ rs[:iminf], fs[:iminf]
+ ).statistic,
+ "spearman-repulsion-energy": stats.spearmanr(
+ rs[imine:], es[imine:]
+ ).statistic,
+ "spearman-attraction-energy": stats.spearmanr(
+ rs[:imine], es[:imine]
+ ).statistic,
+ "pbe-energy-mae": pbe_energy_mae,
+ "pbe-force-mae": pbe_force_mae,
+ }
+
+ df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)
+
+ json_fpath = Path(REGISTRY[model.name]["family"]) / "homonuclear-diatomics.json"
+
+ if json_fpath.exists():
+ df0 = pd.read_json(json_fpath)
+ df = pd.concat([df0, df], ignore_index=True)
+ df.drop_duplicates(inplace=True, subset=["name", "method"], keep="last")
+
+ df.to_json(json_fpath, orient="records")
+
+ json_fpath = Path(model.name) / "homonuclear-diatomics.json"
+ df.to_json(json_fpath, orient="records")
diff --git a/mlip_arena/tasks/diatomics/ani/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/ani/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..cf9b77bd754b058572d651f92e42b9f73a2379c3
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/ani/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f622fde1f33128568baa2beb374e33e112557581a2a5dc675ce8b6273a840a17
+size 121195
diff --git a/mlip_arena/tasks/diatomics/chgnet/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/chgnet/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..642f0114f02c71cfeff92448138c11e659be01f5
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/chgnet/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f3595e1ae02dbc7c30a8ff39227b2a2e46ea940be17e482200f8b3d518cda01
+size 2003404
diff --git a/mlip_arena/tasks/diatomics/equiformer/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/equiformer/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..08515e2c31e5897b922c1b50b2ac07c554f79c50
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/equiformer/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d278cac9a2d7ce073735dcf6e2df6637510efcb8d89289706b54e8b540c942ec
+size 3764511
diff --git a/mlip_arena/tasks/diatomics/escn/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/escn/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..b2730f46fb45d186de89b5d9e101f4732f76bb3a
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/escn/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2bfb8f0a5424259c9e9686e3e8778adb36e9b08937296e2a127d6007a1adf3bf
+size 1960063
diff --git a/mlip_arena/tasks/diatomics/fairchem/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/fairchem/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..f8ad414702846dd5ac901d92ba99f09d8065dd39
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/fairchem/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f955a5e08dbb9be3d0a04f06bd4cf93ac63a6a401132a7e74e971365133472a
+size 4286019
diff --git a/mlip_arena/tasks/diatomics/homonuclear-diatomics.tex b/mlip_arena/tasks/diatomics/homonuclear-diatomics.tex
new file mode 100644
index 0000000000000000000000000000000000000000..1ab4f94f01f882a9a591574e286473b07c7ac614
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/homonuclear-diatomics.tex
@@ -0,0 +1,23 @@
+\begin{tabular}{lrrrrrrrrrrr}
+\toprule
+ & Rank & Rank aggr. & Conservation deviation [eV/โซ] & Spearman's coeff. (E: repulsion) & Spearman's coeff. (F: descending) & Energy jump [eV] & Force flips & Tortuosity \\
+Model & & & & & & & & \\
+\midrule
+MACE-MPA & 1 & 13 & 0.077 & -0.997 & -0.975 & 0.010 & 1.371 & 1.006 \\
+MACE-MP(M) & 2 & 15 & 0.070 & -0.997 & -0.980 & 0.038 & 1.449 & 1.161 \\
+MatterSim & 3 & 20 & 0.013 & -0.980 & -0.972 & 0.008 & 2.766 & 1.021 \\
+M3GNet & 4 & 24 & 0.026 & -0.991 & -0.947 & 0.029 & 3.528 & 1.016 \\
+ORBv2 & 5 & 30 & 9.751 & -0.883 & -0.988 & 0.991 & 0.991 & 1.287 \\
+eSCN(OC20) & 6 & 32 & 2.045 & -0.939 & -0.984 & 0.806 & 0.640 & 5.335 \\
+CHGNet & 7 & 35 & 1.066 & -0.992 & -0.925 & 0.291 & 2.255 & 2.279 \\
+ORB & 8 & 39 & 10.220 & -0.881 & -0.954 & 1.019 & 1.026 & 1.798 \\
+SevenNet & 9 & 41 & 34.005 & -0.986 & -0.928 & 0.392 & 2.112 & 1.292 \\
+eqV2(OMat) & 10 & 51 & 15.477 & -0.880 & -0.976 & 4.118 & 3.126 & 2.515 \\
+eSEN & 11 & 55 & 1.170 & -0.692 & -0.919 & 5.562 & 4.000 & 1.838 \\
+ALIGNN & 12 & 59 & 5.164 & -0.913 & -0.310 & 9.876 & 30.669 & 1.818 \\
+EquiformerV2(OC20) & 13 & 71 & 21.385 & -0.680 & -0.891 & 38.282 & 22.775 & 8.669 \\
+EquiformerV2(OC22) & 14 & 75 & 27.687 & -0.415 & -0.855 & 64.837 & 21.674 & 15.880 \\
+MACE-OFF(M) & 15 & 85 & NaN & NaN & NaN & NaN & NaN & NaN \\
+ANI2x & 16 & 91 & NaN & NaN & NaN & NaN & NaN & NaN \\
+\bottomrule
+\end{tabular}
diff --git a/mlip_arena/tasks/diatomics/m3gnet/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/m3gnet/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..b2dbc565ffc676875303f2360f403e54c7c774ba
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/m3gnet/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c257248d2f62bfd09eb334c49405287bf5bc3bf14cfc6ad0a5890425f559f91c
+size 1854330
diff --git a/mlip_arena/tasks/diatomics/mace-mp/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/mace-mp/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..0201fe283dc93059954f57951d880dde582549dd
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/mace-mp/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ad34875760232ee25a34b7d6e8a54d75e3b8ddd38efaf5de3aa7a3d6e19474d
+size 3837066
diff --git a/mlip_arena/tasks/diatomics/mace-off/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/mace-off/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..af4c0429c938b63bda2bd39a644c303beef804f7
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/mace-off/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:010d9215f9c1765fd2d807c0ef57a95b14832b6bda841e25948c6ee342232579
+size 173998
diff --git a/mlip_arena/tasks/diatomics/matgl/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/matgl/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..ec1a9f68de96b98b29893f3fec035483afe88a1d
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/matgl/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32a7001f8635327e166f7747942ab8fdf4bfb20e68fc632b24005d2017daf5f4
+size 1858414
diff --git a/mlip_arena/tasks/diatomics/mattersim/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/mattersim/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..9bebe3e201c05870616f3bdc5d34de20c713f3d5
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/mattersim/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0181c4173a7ec6cd0ddb469817bc6ff1befec5b9a4a122b5876622ad5006fd49
+size 1929036
diff --git a/mlip_arena/tasks/diatomics/orb/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/orb/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..347b6b0777f99535f22a114ce7907306eebab557
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/orb/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6799123dc798c713ccd100cec528a71b3e3ec4633de8f92c37c0c2d61b5c7801
+size 5197473
diff --git a/mlip_arena/tasks/diatomics/run.ipynb b/mlip_arena/tasks/diatomics/run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6029edac048ca96131a2d0eb419b8e857458e5f5
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/run.ipynb
@@ -0,0 +1,381 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3200850a-b8fb-4f50-9815-16ae8da0f942",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from scipy import stats\n",
+ "from scipy.interpolate import UnivariateSpline\n",
+ "from tqdm.auto import tqdm\n",
+ "\n",
+ "from ase import Atom, Atoms\n",
+ "from ase.data import chemical_symbols, covalent_radii, vdw_alvarez\n",
+ "from ase.io import read, write\n",
+ "from mlip_arena.models import REGISTRY, MLIPEnum\n",
+ "from pymatgen.core import Element"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02ff9cf9-49a2-4cec-80d3-56c6661a513b",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Compute MLIP homonuclear diatomics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "90887faa-1601-4c4c-9c44-d16731471d7f",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "for model in MLIPEnum:\n",
+ " model_name = model.name\n",
+ "\n",
+ " if model_name != \"MACE-MPA\":\n",
+ " continue\n",
+ "\n",
+ " print(f\"========== {model_name} ==========\")\n",
+ "\n",
+ " calc = MLIPEnum[model_name].value()\n",
+ "\n",
+ " for symbol in tqdm(chemical_symbols[1:]):\n",
+ " s = set([symbol])\n",
+ "\n",
+ " if \"X\" in s:\n",
+ " continue\n",
+ "\n",
+ " try:\n",
+ " atom = Atom(symbol)\n",
+ " rmin = 0.9 * covalent_radii[atom.number]\n",
+ " rvdw = (\n",
+ " vdw_alvarez.vdw_radii[atom.number]\n",
+ " if atom.number < len(vdw_alvarez.vdw_radii)\n",
+ " else np.nan\n",
+ " )\n",
+ " rmax = 3.1 * rvdw if not np.isnan(rvdw) else 6\n",
+ " rstep = 0.01\n",
+ " npts = int((rmax - rmin) / rstep)\n",
+ "\n",
+ " rs = np.linspace(rmin, rmax, npts)\n",
+ " es = np.zeros_like(rs)\n",
+ "\n",
+ " da = symbol + symbol\n",
+ "\n",
+ " out_dir = Path(REGISTRY[model_name][\"family\"]) / str(da)\n",
+ " os.makedirs(out_dir, exist_ok=True)\n",
+ "\n",
+ " skip = 0\n",
+ "\n",
+ " element = Element(symbol)\n",
+ "\n",
+ " try:\n",
+ " m = element.valence[1]\n",
+ " if element.valence == (0, 2):\n",
+ " m = 0\n",
+ " except Exception:\n",
+ " m = 0\n",
+ "\n",
+ " a = 2 * rmax\n",
+ " r = rs[0]\n",
+ "\n",
+ " positions = [\n",
+ " [a / 2 - r / 2, a / 2, a / 2],\n",
+ " [a / 2 + r / 2, a / 2, a / 2],\n",
+ " ]\n",
+ "\n",
+ " traj_fpath = out_dir / f\"{model_name}.extxyz\"\n",
+ "\n",
+ " if traj_fpath.exists():\n",
+ " traj = read(traj_fpath, index=\":\")\n",
+ " skip = len(traj)\n",
+ " atoms = traj[-1]\n",
+ " else:\n",
+ " # Create the unit cell with two atoms\n",
+ " atoms = Atoms(\n",
+ " da,\n",
+ " positions=positions,\n",
+ " # magmoms=magmoms,\n",
+ " cell=[a, a + 0.001, a + 0.002],\n",
+ " pbc=True,\n",
+ " )\n",
+ "\n",
+ " print(atoms)\n",
+ "\n",
+ " atoms.calc = calc\n",
+ "\n",
+ " for i, r in enumerate(tqdm(rs)):\n",
+ " if i < skip:\n",
+ " continue\n",
+ "\n",
+ " positions = [\n",
+ " [a / 2 - r / 2, a / 2, a / 2],\n",
+ " [a / 2 + r / 2, a / 2, a / 2],\n",
+ " ]\n",
+ "\n",
+ " # atoms.set_initial_magnetic_moments(magmoms)\n",
+ "\n",
+ " atoms.set_positions(positions)\n",
+ "\n",
+ " es[i] = atoms.get_potential_energy()\n",
+ "\n",
+ " write(traj_fpath, atoms, append=\"a\")\n",
+ " except Exception as e:\n",
+ " print(e)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f1bbfae1-790d-4586-9d7d-79c1ba658dcb",
+ "metadata": {},
+ "source": [
+ "# Analysis and output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a0ac2c09-370b-4fdd-bf74-ea5c4ade0215",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "for model in MLIPEnum:\n",
+ " model_name = model.name\n",
+ "\n",
+ " # if model_name != \"MatterSim\":\n",
+ " # continue\n",
+ "\n",
+ " print(f\"========== {model_name} ==========\")\n",
+ "\n",
+ " df = pd.DataFrame(\n",
+ " columns=[\n",
+ " \"name\",\n",
+ " \"method\",\n",
+ " \"R\",\n",
+ " \"E\",\n",
+ " \"F\",\n",
+ " \"S^2\",\n",
+ " \"force-flip-times\",\n",
+ " \"force-total-variation\",\n",
+ " \"force-jump\",\n",
+ " \"energy-diff-flip-times\",\n",
+ " \"energy-grad-norm-max\",\n",
+ " \"energy-jump\",\n",
+ " \"energy-total-variation\",\n",
+ " \"tortuosity\",\n",
+ " \"conservation-deviation\",\n",
+ " \"spearman-descending-force\",\n",
+ " \"spearman-ascending-force\",\n",
+ " \"spearman-repulsion-energy\",\n",
+ " \"spearman-attraction-energy\",\n",
+ " \"pbe-energy-mae\",\n",
+ " \"pbe-force-mae\",\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ " for symbol in tqdm(chemical_symbols[1:]):\n",
+ " da = symbol + symbol\n",
+ "\n",
+ " out_dir = Path(REGISTRY[model_name][\"family\"]) / da\n",
+ "\n",
+ " traj_fpath = out_dir / f\"{model_name}.extxyz\"\n",
+ "\n",
+ " if traj_fpath.exists():\n",
+ " traj = read(traj_fpath, index=\":\")\n",
+ " else:\n",
+ " continue\n",
+ "\n",
+ " Rs, Es, Fs, S2s = [], [], [], []\n",
+ " for atoms in traj:\n",
+ " vec = atoms.positions[1] - atoms.positions[0]\n",
+ " r = np.linalg.norm(vec)\n",
+ " e = atoms.get_potential_energy()\n",
+ " f = np.inner(vec / r, atoms.get_forces()[1])\n",
+ " # s2 = np.mean(np.power(atoms.get_magnetic_moments(), 2))\n",
+ "\n",
+ " Rs.append(r)\n",
+ " Es.append(e)\n",
+ " Fs.append(f)\n",
+ " # S2s.append(s2)\n",
+ "\n",
+ " rs = np.array(Rs)\n",
+ " es = np.array(Es)\n",
+ " fs = np.array(Fs)\n",
+ "\n",
+ " # sort interatomic distances and align to zero at far field\n",
+ " indices = np.argsort(rs)[::-1]\n",
+ " rs = rs[indices]\n",
+ " es = es[indices]\n",
+ " eshift = es[0]\n",
+ " es -= eshift\n",
+ " fs = fs[indices]\n",
+ "\n",
+ " iminf = np.argmin(fs)\n",
+ " imine = np.argmin(es)\n",
+ "\n",
+ " de_dr = np.gradient(es, rs)\n",
+ " d2e_dr2 = np.gradient(de_dr, rs)\n",
+ "\n",
+ " # avoid numerical sensitity close to zero\n",
+ " rounded_fs = np.copy(fs)\n",
+ " rounded_fs[np.abs(rounded_fs) < 1e-2] = 0 # 10meV/A\n",
+ " fs_sign = np.sign(rounded_fs)\n",
+ " mask = fs_sign != 0\n",
+ " rounded_fs = rounded_fs[mask]\n",
+ " fs_sign = fs_sign[mask]\n",
+ " f_flip = np.diff(fs_sign) != 0\n",
+ "\n",
+ " fdiff = np.diff(fs)\n",
+ " fdiff_sign = np.sign(fdiff)\n",
+ " mask = fdiff_sign != 0\n",
+ " fdiff = fdiff[mask]\n",
+ " fdiff_sign = fdiff_sign[mask]\n",
+ " fdiff_flip = np.diff(fdiff_sign) != 0\n",
+ " fjump = (\n",
+ " np.abs(fdiff[:-1][fdiff_flip]).sum() + np.abs(fdiff[1:][fdiff_flip]).sum()\n",
+ " )\n",
+ "\n",
+ " ediff = np.diff(es)\n",
+ " ediff[np.abs(ediff) < 1e-3] = 0 # 1meV\n",
+ " ediff_sign = np.sign(ediff)\n",
+ " mask = ediff_sign != 0\n",
+ " ediff = ediff[mask]\n",
+ " ediff_sign = ediff_sign[mask]\n",
+ " ediff_flip = np.diff(ediff_sign) != 0\n",
+ " ejump = (\n",
+ " np.abs(ediff[:-1][ediff_flip]).sum() + np.abs(ediff[1:][ediff_flip]).sum()\n",
+ " )\n",
+ "\n",
+ " try:\n",
+ " pbe_traj = read(f\"./vasp/{da}/PBE.extxyz\", index=\":\")\n",
+ "\n",
+ " pbe_rs, pbe_es, pbe_fs = [], [], []\n",
+ "\n",
+ " for atoms in pbe_traj:\n",
+ " vec = atoms.positions[1] - atoms.positions[0]\n",
+ " r = np.linalg.norm(vec)\n",
+ " pbe_rs.append(r)\n",
+ " pbe_es.append(atoms.get_potential_energy())\n",
+ " pbe_fs.append(np.inner(vec / r, atoms.get_forces()[1]))\n",
+ "\n",
+ " pbe_rs = np.array(pbe_rs)\n",
+ " pbe_es = np.array(pbe_es)\n",
+ " pbe_fs = np.array(pbe_fs)\n",
+ "\n",
+ " indices = np.argsort(pbe_rs)\n",
+ " pbe_rs = pbe_rs[indices]\n",
+ " pbe_es = pbe_es[indices]\n",
+ " pbe_fs = pbe_fs[indices]\n",
+ "\n",
+ " pbe_es -= pbe_es[-1]\n",
+ "\n",
+ " xs = np.linspace(pbe_rs.min(), pbe_rs.max(), int(1e3))\n",
+ "\n",
+ " cs = UnivariateSpline(pbe_rs, pbe_es, s=0)\n",
+ " pbe_energy_mae = np.mean(np.abs(es - cs(rs)))\n",
+ "\n",
+ " cs = UnivariateSpline(pbe_rs, pbe_fs, s=0)\n",
+ " pbe_force_mae = np.mean(np.abs(fs - cs(rs)))\n",
+ " except Exception as e:\n",
+ " print(e)\n",
+ " pbe_energy_mae = None\n",
+ " pbe_force_mae = None\n",
+ "\n",
+ " conservation_deviation = np.mean(np.abs(fs + de_dr))\n",
+ "\n",
+ " etv = np.sum(np.abs(np.diff(es)))\n",
+ "\n",
+ " data = {\n",
+ " \"name\": da,\n",
+ " \"method\": model_name,\n",
+ " \"R\": rs,\n",
+ " \"E\": es + eshift,\n",
+ " \"F\": fs,\n",
+ " \"S^2\": S2s,\n",
+ " \"force-flip-times\": np.sum(f_flip),\n",
+ " \"force-total-variation\": np.sum(np.abs(np.diff(fs))),\n",
+ " \"force-jump\": fjump,\n",
+ " \"energy-diff-flip-times\": np.sum(ediff_flip),\n",
+ " \"energy-grad-norm-max\": np.max(np.abs(de_dr)),\n",
+ " \"energy-jump\": ejump,\n",
+ " # \"energy-grad-norm-mean\": np.mean(de_dr_abs),\n",
+ " \"energy-total-variation\": etv,\n",
+ " \"tortuosity\": etv / (abs(es[0] - es.min()) + (es[-1] - es.min())),\n",
+ " \"conservation-deviation\": conservation_deviation,\n",
+ " \"spearman-descending-force\": stats.spearmanr(\n",
+ " rs[iminf:], fs[iminf:]\n",
+ " ).statistic,\n",
+ " \"spearman-ascending-force\": stats.spearmanr(\n",
+ " rs[:iminf], fs[:iminf]\n",
+ " ).statistic,\n",
+ " \"spearman-repulsion-energy\": stats.spearmanr(\n",
+ " rs[imine:], es[imine:]\n",
+ " ).statistic,\n",
+ " \"spearman-attraction-energy\": stats.spearmanr(\n",
+ " rs[:imine], es[:imine]\n",
+ " ).statistic,\n",
+ " \"pbe-energy-mae\": pbe_energy_mae,\n",
+ " \"pbe-force-mae\": pbe_force_mae,\n",
+ " }\n",
+ "\n",
+ " df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+ "\n",
+ " json_fpath = Path(REGISTRY[model_name][\"family\"]) / \"homonuclear-diatomics.json\"\n",
+ "\n",
+ " if json_fpath.exists():\n",
+ " df0 = pd.read_json(json_fpath)\n",
+ " df = pd.concat([df0, df], ignore_index=True)\n",
+ " df.drop_duplicates(inplace=True, subset=[\"name\", \"method\"], keep=\"last\")\n",
+ "\n",
+ " df.to_json(json_fpath, orient=\"records\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "state": {},
+ "version_major": 2,
+ "version_minor": 0
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mlip_arena/tasks/diatomics/run.py b/mlip_arena/tasks/diatomics/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..2564173045360431b16e9eaca53d32eda9df5d45
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/run.py
@@ -0,0 +1,131 @@
+import itertools
+from pathlib import Path
+
+import numpy as np
+from ase import Atom, Atoms
+from ase.calculators.calculator import BaseCalculator
+from ase.data import chemical_symbols, covalent_radii, vdw_alvarez
+from ase.io import read, write
+from prefect import flow, task
+from tqdm.auto import tqdm
+
+from mlip_arena.models import REGISTRY, MLIPEnum
+from mlip_arena.tasks.utils import get_calculator
+
+
+@task
+def homonuclear_diatomics(symbol: str, calculator: BaseCalculator, out_dir: Path):
+ """
+ Calculate potential energy curves for homonuclear diatomic molecules.
+
+ This function computes the potential energy of a diatomic molecule (two atoms of
+ the same element) across a range of interatomic distances. The distance range is
+ automatically determined from the covalent and van der Waals radii of the element.
+
+ Args:
+ symbol: Chemical symbol of the atom (e.g., 'H', 'O', 'Fe')
+ calculator: ASE calculator object used to compute the potential energies. Could be VASP, MLIP, etc.
+
+ Returns:
+ None: Results are saved as trajectory files in a directory structure:
+ /{model_family}/{element_pair}/{model_name}.extxyz
+
+ Note:
+ - Minimum distance is set to 0.9ร the covalent radius
+ - Maximum distance is set to 3.1ร the van der Waals radius (or 6 ร if unknown)
+ - Distance step size is fixed at 0.01 ร
+ - If an existing trajectory file is found, the calculation will resume from where it left off
+ - The atoms are placed in a periodic box large enough to avoid self-interaction
+ """
+
+ atom = Atom(symbol)
+ rmin = 0.9 * covalent_radii[atom.number]
+ rvdw = (
+ vdw_alvarez.vdw_radii[atom.number]
+ if atom.number < len(vdw_alvarez.vdw_radii)
+ else np.nan
+ )
+ rmax = 3.1 * rvdw if not np.isnan(rvdw) else 6
+ rstep = 0.01
+ npts = int((rmax - rmin) / rstep)
+
+ rs = np.linspace(rmin, rmax, npts)
+ es = np.zeros_like(rs)
+
+ da = symbol + symbol
+
+ out_dir.mkdir(parents=True, exist_ok=True)
+
+ skip = 0
+
+ a = 5 * rmax
+ r = rs[0]
+
+ positions = [
+ [a / 2 - r / 2, a / 2, a / 2],
+ [a / 2 + r / 2, a / 2, a / 2],
+ ]
+
+ traj_fpath = out_dir / f"{da!s}.extxyz"
+
+ if traj_fpath.exists():
+ traj = read(traj_fpath, index=":")
+ skip = len(traj)
+ atoms = traj[-1]
+ else:
+ # Create the unit cell with two atoms
+ atoms = Atoms(
+ da,
+ positions=positions,
+ # magmoms=magmoms,
+ cell=[a, a + 0.001, a + 0.002],
+ pbc=False,
+ )
+
+ atoms.calc = calculator
+
+ for i, r in enumerate(tqdm(rs)):
+ if i < skip:
+ continue
+
+ positions = [
+ [a / 2 - r / 2, a / 2, a / 2],
+ [a / 2 + r / 2, a / 2, a / 2],
+ ]
+
+ # atoms.set_initial_magnetic_moments(magmoms)
+ atoms.set_positions(positions)
+ es[i] = atoms.get_potential_energy()
+ write(traj_fpath, atoms, append="a")
+
+
+@flow
+def submit_homonuclear_diatomics():
+ futures = []
+ for symbol, model in itertools.product(
+ chemical_symbols[1:],
+ MLIPEnum,
+ ):
+ if "homonuclear-diatomics" not in REGISTRY[model.name].get("gpu-tasks", []):
+ continue
+
+ out_dir = Path(__file__).parent / model.name
+
+ calculator = get_calculator(model)
+
+ # if not (out_dir / "homonuclear-diatomics.json").exists():
+ future = homonuclear_diatomics.submit(
+ symbol,
+ calculator,
+ out_dir=out_dir,
+ )
+ futures.append(future)
+
+ return [f.result(raise_on_failure=False) for f in futures]
+
+
+if __name__ == "__main__":
+ submit_homonuclear_diatomics.with_options(
+ # task_runner=DaskTaskRunner(address=client.scheduler.address),
+ log_prints=True,
+ )()
diff --git a/mlip_arena/tasks/diatomics/sevennet/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/sevennet/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..0c9b1e0aeb7444568f34e8327d8f71bcb73d094c
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/sevennet/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1cb205f2c271f1799a03ff33ff23e9a1acd9a2d8162d232ba7d3aa59f0a1b30e
+size 1859865
diff --git a/mlip_arena/tasks/diatomics/vasp/homonuclear-diatomics.json b/mlip_arena/tasks/diatomics/vasp/homonuclear-diatomics.json
new file mode 100644
index 0000000000000000000000000000000000000000..f711533d9a4dd8029396d88156b055dd6dd31ac1
--- /dev/null
+++ b/mlip_arena/tasks/diatomics/vasp/homonuclear-diatomics.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:26fc88d6d1777e6861bd2d5e5045a932a5ad8fe0a75e8e8848dca481c48cc617
+size 16606
diff --git a/mlip_arena/tasks/elasticity.py b/mlip_arena/tasks/elasticity.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a34e7de6ab250db70e69df40c42e81e3cee4be1
--- /dev/null
+++ b/mlip_arena/tasks/elasticity.py
@@ -0,0 +1,216 @@
+"""
+Defines the tasks for computing the elastic tensor.
+
+This module has been modified from MatCalc
+https://github.com/materialsvirtuallab/matcalc/blob/main/src/matcalc/elasticity.py
+
+https://github.com/materialsvirtuallab/matcalc/blob/main/LICENSE
+
+BSD 3-Clause License
+
+Copyright (c) 2023, Materials Virtual Lab
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+ list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+ this list of conditions and the following disclaimer in the documentation
+ and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+ contributors may be used to endorse or promote products derived from
+ this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+from ase import Atoms
+from ase.calculators.calculator import BaseCalculator
+from ase.optimize.optimize import Optimizer
+from numpy.typing import ArrayLike
+from prefect import task
+from prefect.cache_policies import INPUTS, TASK_SOURCE
+from prefect.runtime import task_run
+from prefect.states import State
+
+from mlip_arena.tasks.optimize import run as OPT
+from pymatgen.analysis.elasticity import DeformedStructureSet, ElasticTensor, Strain
+from pymatgen.analysis.elasticity.elastic import get_strain_state_dict
+from pymatgen.io.ase import AseAtomsAdaptor
+
+if TYPE_CHECKING:
+ from ase.filters import Filter
+
+
+def _generate_task_run_name():
+ task_name = task_run.task_name
+ parameters = task_run.parameters
+
+ atoms = parameters["atoms"]
+ calculator_name = parameters["calculator"]
+
+ return f"{task_name}: {atoms.get_chemical_formula()} - {calculator_name}"
+
+
+@task(
+ name="Elasticity",
+ task_run_name=_generate_task_run_name,
+ cache_policy=TASK_SOURCE + INPUTS,
+ # cache_key_fn=task_input_hash,
+)
+def run(
+ atoms: Atoms,
+ calculator: BaseCalculator,
+ optimizer: Optimizer | str = "BFGSLineSearch", # type: ignore
+ optimizer_kwargs: dict | None = None,
+ filter: Filter | str | None = "FrechetCell", # type: ignore
+ filter_kwargs: dict | None = None,
+ criterion: dict | None = None,
+ normal_strains: list[float] | np.ndarray | None = np.linspace(-0.01, 0.01, 4),
+ shear_strains: list[float] | np.ndarray | None = np.linspace(-0.06, 0.06, 4),
+ persist_opt: bool = True,
+ cache_opt: bool = False,
+) -> dict[str, Any] | State:
+ """
+ Compute the elastic tensor for the given structure and calculator.
+
+ Args:
+ atoms (Atoms): The input structure.
+ calculator (BaseCalculator): The calculator.
+ optimizer (Optimizer | str, optional): The optimizer. Defaults to "BFGSLineSearch".
+ optimizer_kwargs (dict, optional): The optimizer kwargs. Defaults to None.
+ filter (Filter | str, optional): The filter. Defaults to "FrechetCell".
+ filter_kwargs (dict, optional): The filter kwargs. Defaults to None.
+ criterion (dict, optional): The criterion. Defaults to None.
+ normal_strains (list[float] | np.ndarray, optional): The normal strains. Defaults to np.linspace(-0.01, 0.01, 4).
+ shear_strains (list[float] | np.ndarray, optional): The shear strains. Defaults to np.linspace(-0.06, 0.06, 4).
+ concurrent (bool, optional): Whether to run concurrently. Defaults to True.
+ persist_opt (bool, optional): Whether to persist the optimizer results. Defaults to True.
+ cache_opt (bool, optional): Whether to cache the optimizer results. Defaults to True.
+
+ Returns:
+ dict[str, Any] | State: The elastic tensor.
+ """
+
+ atoms = atoms.copy()
+
+ OPT_ = OPT.with_options(
+ refresh_cache=not cache_opt,
+ persist_result=persist_opt,
+ )
+
+ first_relax = OPT_(
+ atoms=atoms,
+ calculator=calculator,
+ optimizer=optimizer,
+ optimizer_kwargs=optimizer_kwargs,
+ filter=filter,
+ filter_kwargs=filter_kwargs,
+ criterion=criterion,
+ return_state=True,
+ )
+
+ if first_relax.is_failed():
+ return first_relax
+
+ result = first_relax.result(raise_on_failure=False)
+
+ assert isinstance(result, dict)
+ relaxed = result["atoms"]
+
+ if isinstance(normal_strains, np.ndarray):
+ normal_strains = normal_strains.tolist()
+ if isinstance(shear_strains, np.ndarray):
+ shear_strains = shear_strains.tolist()
+
+ assert isinstance(relaxed, Atoms)
+ assert isinstance(normal_strains, list)
+ assert isinstance(shear_strains, list)
+
+ structure = AseAtomsAdaptor.get_structure(relaxed) # type: ignore
+
+ deformed_structure_set = DeformedStructureSet(
+ structure,
+ normal_strains,
+ shear_strains,
+ )
+
+ stresses = []
+ for deformed_structure in deformed_structure_set:
+ atoms = deformed_structure.to_ase_atoms()
+ atoms.calc = relaxed.calc
+ stresses.append(atoms.get_stress(voigt=False))
+
+ strains = [
+ Strain.from_deformation(deformation)
+ for deformation in deformed_structure_set.deformations
+ ]
+
+ fit = fit_elastic_tensor(
+ strains, stresses, eq_stress=relaxed.get_stress(voigt=False)
+ )
+
+ return {
+ "elastic_tensor": fit["elastic_tensor"],
+ "residuals_sum": fit["residuals_sum"],
+ }
+
+
+@task
+def fit_elastic_tensor(
+ strains: ArrayLike,
+ stresses: ArrayLike,
+ eq_stress: ArrayLike | None = None,
+ tolerance: float = 1e-7,
+):
+ """
+ Compute the elastic tensor from the given strains and stresses.
+
+ Args:
+ strains (ArrayLike): The strains.
+ stresses (ArrayLike): The stresses.
+ tolerance (float, optional): The tolerance. Defaults to 1e-7.
+
+ Returns:
+ ElasticTensor: The elastic tensor.
+ """
+
+ strain_states = [tuple(ss) for ss in np.eye(6)]
+ ss_dict = get_strain_state_dict(
+ strains,
+ stresses,
+ eq_stress=eq_stress,
+ add_eq=True if eq_stress is not None else False,
+ )
+ c_ij = np.zeros((6, 6))
+ residuals_sum = 0.0
+ for ii in range(6):
+ strain = ss_dict[strain_states[ii]]["strains"]
+ stress = ss_dict[strain_states[ii]]["stresses"]
+ for jj in range(6):
+ fit = np.polyfit(strain[:, ii], stress[:, jj], 1, full=True)
+ c_ij[ii, jj] = fit[0][0]
+ residuals_sum += fit[1][0] if len(fit[1]) > 0 else 0.0
+ elastic_tensor = ElasticTensor.from_voigt(c_ij)
+
+ return {
+ "elastic_tensor": elastic_tensor.zeroed(tolerance),
+ "residuals_sum": residuals_sum,
+ }
diff --git a/mlip_arena/tasks/eos.py b/mlip_arena/tasks/eos.py
new file mode 100644
index 0000000000000000000000000000000000000000..64d53fda005783ebf2effa64329b85ef366ebcb9
--- /dev/null
+++ b/mlip_arena/tasks/eos.py
@@ -0,0 +1,179 @@
+"""
+Define equation of state task.
+
+https://github.com/materialsvirtuallab/matcalc/blob/main/matcalc/eos.py
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+from ase import Atoms
+from ase.calculators.calculator import BaseCalculator
+from ase.optimize.optimize import Optimizer
+from prefect import task
+from prefect.cache_policies import INPUTS, TASK_SOURCE
+from prefect.futures import wait
+from prefect.results import ResultRecord
+from prefect.runtime import task_run
+from prefect.states import State
+
+from mlip_arena.tasks.optimize import run as OPT
+from pymatgen.analysis.eos import BirchMurnaghan
+
+if TYPE_CHECKING:
+ from ase.filters import Filter
+
+
+def _generate_task_run_name():
+ task_name = task_run.task_name
+ parameters = task_run.parameters
+
+ atoms = parameters["atoms"]
+ calculator_name = parameters["calculator"]
+
+ return f"{task_name}: {atoms.get_chemical_formula()} - {calculator_name}"
+
+
+@task(
+ name="EOS", task_run_name=_generate_task_run_name, cache_policy=TASK_SOURCE + INPUTS
+)
+def run(
+ atoms: Atoms,
+ calculator: BaseCalculator,
+ optimizer: Optimizer | str = "BFGSLineSearch", # type: ignore
+ optimizer_kwargs: dict | None = None,
+ filter: Filter | str | None = "FrechetCell", # type: ignore
+ filter_kwargs: dict | None = None,
+ criterion: dict | None = None,
+ max_abs_strain: float = 0.1,
+ npoints: int = 11,
+ concurrent: bool = True,
+ cache_opt: bool = False,
+) -> dict[str, Any] | State:
+ """
+ Compute the equation of state (EOS) for the given atoms and calculator.
+
+ Args:
+ atoms: The input atoms.
+ calculator_name: The name of the calculator to use.
+ calculator_kwargs: Additional kwargs to pass to the calculator.
+ device: The device to use.
+ optimizer: The optimizer to use.
+ optimizer_kwargs: Additional kwargs to pass to the optimizer.
+ filter: The filter to use.
+ filter_kwargs: Additional kwargs to pass to the filter.
+ criterion: The criterion to use.
+ max_abs_strain: The maximum absolute strain to use.
+ npoints: The number of points to sample.
+ concurrent: Whether to relax multiple structures concurrently.
+ persist_opt: Whether to persist the optimization results.
+ cache_opt: Whether to cache the intermediate optimization results.
+
+ Returns:
+ A dictionary containing the EOS data, bulk modulus, equilibrium volume, and equilibrium energy if successful. Otherwise, a prefect state object.
+ """
+
+ atoms = atoms.copy()
+
+ OPT_ = OPT.with_options(
+ refresh_cache=not cache_opt,
+ persist_result=cache_opt,
+ )
+
+ state = OPT_(
+ atoms=atoms,
+ calculator=calculator,
+ optimizer=optimizer,
+ optimizer_kwargs=optimizer_kwargs,
+ filter=filter,
+ filter_kwargs=filter_kwargs,
+ criterion=criterion,
+ return_state=True,
+ )
+
+ if state.is_failed():
+ return state
+
+ first_relax = state.result(raise_on_failure=False)
+
+ if isinstance(first_relax, ResultRecord):
+ relaxed = first_relax.result["atoms"]
+ else:
+ relaxed = first_relax["atoms"]
+
+ # p0 = relaxed.get_positions()
+ c0 = relaxed.get_cell()
+
+ factors = np.linspace(1 - max_abs_strain, 1 + max_abs_strain, npoints) ** (1 / 3)
+
+ if concurrent:
+ futures = []
+ for f in factors:
+ atoms = relaxed.copy()
+ atoms.set_cell(c0 * f, scale_atoms=True)
+
+ future = OPT_.submit(
+ atoms=atoms,
+ calculator=calculator,
+ optimizer=optimizer,
+ optimizer_kwargs=optimizer_kwargs,
+ filter=None,
+ filter_kwargs=None,
+ criterion=criterion,
+ )
+ futures.append(future)
+
+ wait(futures)
+
+ results = [
+ f.result(raise_on_failure=False)
+ for f in futures
+ if future.state.is_completed()
+ ]
+ else:
+ states = []
+ for f in factors:
+ atoms = relaxed.copy()
+ atoms.set_cell(c0 * f, scale_atoms=True)
+
+ state = OPT_(
+ atoms=atoms,
+ calculator=calculator,
+ optimizer=optimizer,
+ optimizer_kwargs=optimizer_kwargs,
+ filter=None,
+ filter_kwargs=None,
+ criterion=criterion,
+ return_state=True,
+ )
+ states.append(state)
+
+ results = [s.result(raise_on_failure=False) for s in states if s.is_completed()]
+
+ results = [r.result if isinstance(r, ResultRecord) else r for r in results]
+
+ volumes = [r["atoms"].get_volume() for r in results]
+ energies = [r["atoms"].get_potential_energy() for r in results]
+
+ volumes, energies = map(
+ list,
+ zip(
+ *sorted(zip(volumes, energies, strict=True), key=lambda i: i[0]),
+ strict=True,
+ ),
+ )
+
+ bm = BirchMurnaghan(volumes=volumes, energies=energies)
+ bm.fit()
+
+ return {
+ "atoms": relaxed,
+ "eos": {"volumes": volumes, "energies": energies},
+ "K": bm.b0_GPa,
+ "b0": bm.b0,
+ "b1": bm.b1,
+ "e0": bm.e0,
+ "v0": bm.v0,
+ }
diff --git a/mlip_arena/tasks/eos_alloy/__init__.py b/mlip_arena/tasks/eos_alloy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..65ce79a9418728dc6ca9d92372643ca5d1d35486
--- /dev/null
+++ b/mlip_arena/tasks/eos_alloy/__init__.py
@@ -0,0 +1,12 @@
+from pathlib import Path
+
+from loguru import logger
+
+license_path = Path(__file__).parent / "LICENSE"
+
+logger.info(f"""
+This module {__name__} is kindly shared by @jan-janssen . If you use this module, you agree to cite the reference
+
+- Alvi, S. M. A. A., Janssen, J., Khatamsaz, D., Perez, D., Allaire, D., & Arroyave, R. (2024). Hierarchical Gaussian Process-Based Bayesian Optimization for Materials Discovery in High Entropy Alloy Spaces. *arXiv preprint arXiv:2410.04314*.
+- Gehringer, D., Friรกk, M., & Holec, D. (2023). Models of configurationally-complex alloys made simple. *Computer Physics Communications, 286*, 108664.
+""")
\ No newline at end of file
diff --git a/mlip_arena/tasks/eos_alloy/flow.py b/mlip_arena/tasks/eos_alloy/flow.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b26373ea979a037499152d0b04cb361795ff19e
--- /dev/null
+++ b/mlip_arena/tasks/eos_alloy/flow.py
@@ -0,0 +1,145 @@
+from functools import partial
+from pathlib import Path
+
+import pandas as pd
+from huggingface_hub import hf_hub_download
+from prefect import Task, flow, task
+from prefect.client.schemas.objects import TaskRun
+from prefect.futures import wait
+from prefect.states import State
+
+from ase.db import connect
+from mlip_arena.data.local import SafeHDFStore
+from mlip_arena.models import REGISTRY, MLIPEnum
+from mlip_arena.tasks.eos import run as EOS
+
+
+@task
+def get_atoms_from_db(db_path: Path | str):
+ db_path = Path(db_path)
+ if not db_path.exists():
+ db_path = hf_hub_download(
+ repo_id="atomind/mlip-arena",
+ repo_type="dataset",
+ subfolder=f"{Path(__file__).parent.name}",
+ filename=str(db_path),
+ )
+ with connect(db_path) as db:
+ for row in db.select():
+ yield row.toatoms()
+
+
+def save_to_hdf(
+ tsk: Task, run: TaskRun, state: State, fpath: Path | str, table_name: str
+):
+ """
+ Define a hook on completion of EOS task to save results to HDF5 file.
+ """
+
+ if run.state.is_failed():
+ return
+
+ result = run.state.result(raise_on_failure=False)
+
+ if not isinstance(result, dict):
+ return
+
+ try:
+ atoms = result["atoms"]
+ calculator_name = (
+ run.task_inputs["calculator_name"] or result["calculator_name"]
+ )
+
+ energies = [float(e) for e in result["eos"]["energies"]]
+
+ formula = atoms.get_chemical_formula()
+
+ df = pd.DataFrame(
+ {
+ "method": calculator_name,
+ "formula": formula,
+ "total_run_time": run.total_run_time,
+ "v0": result["v0"],
+ "e0": result["e0"],
+ "b0": result["b0"],
+ "b1": result["b1"],
+ "volume": result["eos"]["volumes"],
+ "energy": energies,
+ }
+ )
+
+ fpath = Path(fpath)
+ fpath = fpath.with_stem(fpath.stem + f"_{calculator_name}")
+
+ family_path = Path(__file__).parent / REGISTRY[calculator_name]["family"]
+ family_path.mkdir(parents=True, exist_ok=True)
+
+ df.to_json(family_path / f"{calculator_name}_{formula}.json", indent=2)
+
+ with SafeHDFStore(fpath, mode="a") as store:
+ store.append(
+ table_name,
+ df,
+ format="table",
+ data_columns=True,
+ min_itemsize={"formula": 50, "method": 20},
+ )
+ except Exception as e:
+ print(e)
+
+
+@flow(
+ name="EOS Alloy"
+)
+def run(
+ db_path: Path | str,
+ out_path: Path | str,
+ table_name: str,
+ optimizer="FIRE",
+ optimizer_kwargs=None,
+ filter="FrechetCell",
+ filter_kwargs=None,
+ criterion=dict(fmax=0.1, steps=1000),
+ max_abs_strain=0.20,
+ concurrent=False,
+ cache=True,
+):
+ EOS_ = EOS.with_options(
+ on_completion=[partial(save_to_hdf, fpath=out_path, table_name=table_name)],
+ refresh_cache=not cache,
+ )
+
+ futures = []
+ for atoms in get_atoms_from_db(db_path):
+ for mlip in MLIPEnum:
+ if not REGISTRY[mlip.name]["npt"]:
+ continue
+ if Path(__file__).parent.name not in (
+ REGISTRY[mlip.name].get("cpu-tasks", [])
+ + REGISTRY[mlip.name].get("gpu-tasks", [])
+ ):
+ continue
+ future = EOS_.submit(
+ atoms=atoms,
+ calculator_name=mlip.name,
+ calculator_kwargs=dict(),
+ optimizer=optimizer,
+ optimizer_kwargs=optimizer_kwargs,
+ filter=filter,
+ filter_kwargs=filter_kwargs,
+ criterion=criterion,
+ max_abs_strain=max_abs_strain,
+ concurrent=concurrent,
+ persist_opt=cache,
+ cache_opt=cache,
+ # return_state=True
+ )
+ futures.append(future)
+
+ wait(futures)
+
+ return [
+ f.result(timeout=None, raise_on_failure=False)
+ for f in futures
+ if f.state.is_completed()
+ ]
diff --git a/mlip_arena/tasks/eos_alloy/input.py b/mlip_arena/tasks/eos_alloy/input.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c120fd5086986c1f47fe69709e2e2445faf29ba
--- /dev/null
+++ b/mlip_arena/tasks/eos_alloy/input.py
@@ -0,0 +1,179 @@
+"""
+Generates a database of special quasi-random structures (SQS) from a template structure.
+
+This script utilizes the `structuretoolkit `_
+to call `sqsgenerator `_ to generate
+SQS structures. The generated structures are saved to an ASE database file and optionally uploaded
+to the Hugging Face Hub.
+
+References
+~~~~~~~~~~
+- Alvi, S. M. A. A., Janssen, J., Khatamsaz, D., Perez, D., Allaire, D., & Arroyave, R. (2024).
+ Hierarchical Gaussian Process-Based Bayesian Optimization for Materials Discovery in High
+ Entropy Alloy Spaces. *arXiv preprint arXiv:2410.04314*.
+- Gehringer, D., Friรกk, M., & Holec, D. (2023). Models of configurationally-complex alloys made
+ simple. *Computer Physics Communications, 286*, 108664.
+
+Authors
+~~~~~~~
+- Jan Janssen (`@jan-janssen `_)
+- Yuan Chiang (`@chiang-yuan `_)
+"""
+
+import os
+from pathlib import Path
+from typing import Generator, Iterable
+
+import numpy as np
+from huggingface_hub import HfApi, hf_hub_download
+from prefect import task
+from tqdm.auto import tqdm
+
+from ase import Atoms
+from ase.db import connect
+
+
+def save_to_db(
+ atoms_list: list[Atoms] | Iterable[Atoms] | Atoms,
+ db_path: Path | str,
+ upload: bool = True,
+ hf_token: str | None = os.getenv("HF_TOKEN", None),
+ repo_id: str = "atomind/mlip-arena",
+ repo_type: str = "dataset",
+ subfolder: str = Path(__file__).parent.name,
+):
+ """Save ASE Atoms objects to an ASE database and optionally upload to Hugging Face Hub."""
+
+ if upload and hf_token is None:
+ raise ValueError("HF_TOKEN is required to upload the database.")
+
+ db_path = Path(db_path)
+
+ if isinstance(atoms_list, Atoms):
+ atoms_list = [atoms_list]
+
+ with connect(db_path) as db:
+ for atoms in atoms_list:
+ if not isinstance(atoms, Atoms):
+ raise ValueError("atoms_list must contain ASE Atoms objects.")
+ db.write(atoms)
+
+ if upload:
+ api = HfApi(token=hf_token)
+ api.upload_file(
+ path_or_fileobj=db_path,
+ path_in_repo=f"{subfolder}/{db_path.name}",
+ repo_id=repo_id,
+ repo_type=repo_type,
+ )
+ print(f"{db_path.name} uploaded to {repo_id}/{subfolder}")
+
+ return db_path
+
+@task
+def get_atoms_from_db(
+ db_path: Path | str,
+ repo_id: str = "atomind/mlip-arena",
+ repo_type: str = "dataset",
+ subfolder: str = Path(__file__).parent.name,
+) -> Generator[Atoms, None, None]:
+ """Retrieve ASE Atoms objects from an ASE database."""
+ db_path = Path(db_path)
+ if not db_path.exists():
+ db_path = hf_hub_download(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ subfolder=subfolder,
+ filename=str(db_path),
+ )
+ with connect(db_path) as db:
+ for row in db.select():
+ yield row.toatoms()
+
+
+def body_order(n=32, b=5):
+ """
+ Generate all possible combinations of atomic counts for `b` species
+ that sum to `n`.
+ """
+ if b == 2:
+ return [[i, n - i] for i in range(n + 1)]
+ return [[i] + j for i in range(n + 1) for j in body_order(n=n - i, b=b - 1)]
+
+
+def generate_sqs(structure_template, elements, counts):
+ """
+ Generate a special quasi-random structure (SQS) based on mole fractions.
+ """
+ import structuretoolkit as stk
+
+ mole_fractions = {
+ el: c / len(structure_template) for el, c in zip(elements, counts)
+ }
+ return stk.build.sqs_structures(
+ structure=structure_template,
+ mole_fractions=mole_fractions,
+ )[0]
+
+
+def get_endmember(structure, conc_lst, elements):
+ """
+ Assign a single element to all atoms in the structure to create an endmember.
+ """
+ structure.symbols[:] = np.array(elements)[conc_lst != 0][0]
+ return structure
+
+
+def generate_alloy_db(
+ structure_template: Atoms,
+ elements: list[str],
+ db_path: Path | str,
+ upload: bool = True,
+ hf_token: str | None = os.getenv("HF_TOKEN", None),
+ repo_id: str = "atomind/mlip-arena",
+ repo_type: str = "dataset",
+) -> Path:
+
+ if upload and hf_token is None:
+ raise ValueError("HF_TOKEN is required to upload the database.")
+
+ num_atoms = len(structure_template)
+ num_species = len(elements)
+
+ # Generate all possible atomic configurations
+ configurations = np.array(body_order(n=num_atoms, b=num_species))
+
+ # Prepare the database
+ db_path = (
+ Path(db_path) or Path(__file__).resolve().parent / f"sqs_{'-'.join(elements)}.db"
+ )
+ db_path.unlink(missing_ok=True)
+
+ atoms_list = []
+ for i, composition in tqdm(
+ enumerate(configurations), total=len(configurations)
+ ):
+ # Skip trivial cases where only one element is present
+ if sum(composition == 0) != len(elements) - 1:
+ atoms = generate_sqs(
+ structure_template=structure_template,
+ elements=np.array(elements)[composition != 0],
+ counts=composition[composition != 0],
+ )
+ else:
+ atoms = get_endmember(
+ structure=structure_template.copy(),
+ conc_lst=composition,
+ elements=elements,
+ )
+ atoms_list.append(atoms)
+
+
+ return save_to_db(
+ atoms_list=atoms_list,
+ db_path=db_path,
+ upload=upload,
+ hf_token=hf_token,
+ repo_id=repo_id,
+ repo_type=repo_type,
+ )
diff --git a/mlip_arena/tasks/md.py b/mlip_arena/tasks/md.py
new file mode 100644
index 0000000000000000000000000000000000000000..b23a8958d1da1c6708c8b1e7f94a0dba890045dc
--- /dev/null
+++ b/mlip_arena/tasks/md.py
@@ -0,0 +1,390 @@
+"""
+Define molecular dynamics task.
+
+This script has been adapted from Atomate2 MLFF MD workflow written by Aaron Kaplan and Yuan Chiang
+https://github.com/materialsproject/atomate2/blob/main/src/atomate2/forcefields/md.py
+
+atomate2 Copyright (c) 2015, The Regents of the University of
+California, through Lawrence Berkeley National Laboratory (subject
+to receipt of any required approvals from the U.S. Dept. of Energy).
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+(1) Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+(2) Redistributions in binary form must reproduce the above
+copyright notice, this list of conditions and the following
+disclaimer in the documentation and/or other materials provided with
+the distribution.
+
+(3) Neither the name of the University of California, Lawrence
+Berkeley National Laboratory, U.S. Dept. of Energy nor the names of
+its contributors may be used to endorse or promote products derived
+from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+
+You are under no obligation whatsoever to provide any bug fixes,
+patches, or upgrades to the features, functionality or performance
+of the source code ("Enhancements") to anyone; however, if you
+choose to make your Enhancements available either publicly, or
+directly to Lawrence Berkeley National Laboratory or its
+contributors, without imposing a separate written license agreement
+for such Enhancements, then you hereby grant the following license:
+a non-exclusive, royalty-free perpetual license to install, use,
+modify, prepare derivative works, incorporate into other computer
+software, distribute, and sublicense such enhancements or derivative
+works thereof, in binary and source code form.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from datetime import datetime
+from pathlib import Path
+from typing import Literal
+
+import numpy as np
+from ase import Atoms, units
+from ase.calculators.calculator import BaseCalculator
+from ase.io import read
+from ase.io.trajectory import Trajectory
+from ase.md.andersen import Andersen
+from ase.md.langevin import Langevin
+from ase.md.md import MolecularDynamics
+from ase.md.npt import NPT
+from ase.md.nptberendsen import NPTBerendsen
+from ase.md.nvtberendsen import NVTBerendsen
+from ase.md.velocitydistribution import (
+ MaxwellBoltzmannDistribution,
+ Stationary,
+ ZeroRotation,
+)
+from ase.md.verlet import VelocityVerlet
+from prefect import task
+from prefect.cache_policies import INPUTS, TASK_SOURCE
+from prefect.runtime import task_run
+from scipy.interpolate import interp1d
+from scipy.linalg import schur
+from tqdm.auto import tqdm
+
+_valid_dynamics: dict[str, tuple[str, ...]] = {
+ "nve": ("velocityverlet",),
+ "nvt": ("nose-hoover", "langevin", "andersen", "berendsen"),
+ "npt": ("nose-hoover", "berendsen"),
+}
+
+_preset_dynamics: dict = {
+ "nve_velocityverlet": VelocityVerlet,
+ "nvt_andersen": Andersen,
+ "nvt_berendsen": NVTBerendsen,
+ "nvt_langevin": Langevin,
+ "nvt_nose-hoover": NPT,
+ "npt_berendsen": NPTBerendsen,
+ "npt_nose-hoover": NPT,
+}
+
+
+def _interpolate_quantity(values: Sequence | np.ndarray, n_pts: int) -> np.ndarray:
+ """Interpolate temperature / pressure on a schedule."""
+ n_vals = len(values)
+ return np.interp(
+ np.linspace(0, n_vals - 1, n_pts + 1),
+ np.linspace(0, n_vals - 1, n_vals),
+ values,
+ )
+
+
+def _get_ensemble_schedule(
+ ensemble: Literal["nve", "nvt", "npt"] = "nvt",
+ n_steps: int = 1000,
+ temperature: float | Sequence | np.ndarray | None = 300.0,
+ pressure: float | Sequence | np.ndarray | None = None,
+) -> tuple[np.ndarray, np.ndarray]:
+ if ensemble == "nve":
+ # Disable thermostat and barostat
+ temperature = np.nan
+ pressure = np.nan
+ t_schedule = np.full(n_steps + 1, temperature)
+ p_schedule = np.full(n_steps + 1, pressure)
+ return t_schedule, p_schedule
+
+ if isinstance(temperature, Sequence) or (
+ isinstance(temperature, np.ndarray) and temperature.ndim == 1
+ ):
+ t_schedule = _interpolate_quantity(temperature, n_steps)
+ # NOTE: In ASE Langevin dynamics, the temperature are normally
+ # scalars, but in principle one quantity per atom could be specified by giving
+ # an array. This is not implemented yet here.
+ else:
+ t_schedule = np.full(n_steps + 1, temperature)
+
+ if ensemble == "nvt":
+ pressure = np.nan
+ p_schedule = np.full(n_steps + 1, pressure)
+ return t_schedule, p_schedule
+
+ if isinstance(pressure, Sequence) or (
+ isinstance(pressure, np.ndarray) and pressure.ndim == 1
+ ):
+ p_schedule = _interpolate_quantity(pressure, n_steps)
+ elif isinstance(pressure, np.ndarray) and pressure.ndim == 3:
+ p_schedule = interp1d(np.arange(n_steps + 1), pressure, kind="linear")
+ assert isinstance(p_schedule, np.ndarray)
+ else:
+ p_schedule = np.full(n_steps + 1, pressure)
+
+ return t_schedule, p_schedule
+
+
+def _get_ensemble_defaults(
+ ensemble: Literal["nve", "nvt", "npt"],
+ dynamics: str | MolecularDynamics,
+ t_schedule: np.ndarray,
+ p_schedule: np.ndarray,
+ dynamics_kwargs: dict | None = None,
+) -> dict:
+ """Update ASE MD kwargs"""
+ dynamics_kwargs = dynamics_kwargs or {}
+
+ if ensemble == "nve":
+ dynamics_kwargs.pop("temperature", None)
+ dynamics_kwargs.pop("temperature_K", None)
+ dynamics_kwargs.pop("externalstress", None)
+ elif ensemble == "nvt":
+ dynamics_kwargs["temperature_K"] = t_schedule[0]
+ dynamics_kwargs.pop("externalstress", None)
+ elif ensemble == "npt":
+ dynamics_kwargs["temperature_K"] = t_schedule[0]
+ dynamics_kwargs["externalstress"] = p_schedule[0] # * 1e3 * units.bar
+
+ if isinstance(dynamics, str) and dynamics.lower() == "langevin":
+ dynamics_kwargs["friction"] = dynamics_kwargs.get(
+ "friction",
+ 10.0 * 1e-3 / units.fs, # Same default as in VASP: 10 ps^-1
+ )
+
+ return dynamics_kwargs
+
+
+def _generate_task_run_name():
+ task_name = task_run.task_name
+ parameters = task_run.parameters
+
+ atoms = parameters["atoms"]
+ calculator = parameters["calculator"]
+
+ return f"{task_name}: {atoms.get_chemical_formula()} - {calculator}"
+
+
+@task(
+ name="MD", task_run_name=_generate_task_run_name, cache_policy=TASK_SOURCE + INPUTS
+)
+def run(
+ atoms: Atoms,
+ calculator: BaseCalculator,
+ ensemble: Literal["nve", "nvt", "npt"] = "nvt",
+ dynamics: str | MolecularDynamics = "langevin",
+ time_step: float | None = None, # fs
+ total_time: float = 1000, # fs
+ temperature: float | Sequence | np.ndarray | None = 300.0, # K
+ pressure: float | Sequence | np.ndarray | None = None, # eV/A^3
+ dynamics_kwargs: dict | None = None,
+ velocity_seed: int | None = None,
+ zero_linear_momentum: bool = True,
+ zero_angular_momentum: bool = True,
+ traj_file: str | Path | None = None,
+ traj_interval: int = 1,
+ restart: bool = True,
+):
+ """
+ Run a molecular dynamics (MD) simulation using ASE.
+
+ Parameters:
+ atoms (Atoms): The atomic structure to simulate.
+ calculator (BaseCalculator): The calculator to use for energy and force calculations.
+ ensemble (Literal["nve", "nvt", "npt"], optional): The MD ensemble to use. Defaults to "nvt".
+ dynamics (str | MolecularDynamics, optional): The dynamics method to use. Defaults to "langevin".
+ time_step (float | None, optional): The time step for the simulation in femtoseconds.
+ Defaults to 0.5 fs if hydrogen isotopes are present, otherwise 2.0 fs.
+ total_time (float, optional): The total simulation time in femtoseconds. Defaults to 1000 fs.
+ temperature (float | Sequence | np.ndarray | None, optional): The temperature schedule in Kelvin.
+ Can be a scalar or a sequence. Defaults to 300 K.
+ pressure (float | Sequence | np.ndarray | None, optional): The pressure schedule in eV/ร ยณ.
+ Can be a scalar or a sequence. Defaults to None.
+ dynamics_kwargs (dict | None, optional): Additional keyword arguments for the dynamics method. Defaults to None.
+ velocity_seed (int | None, optional): Seed for random number generation for initial velocities. Defaults to None.
+ zero_linear_momentum (bool, optional): Whether to remove linear momentum from the system. Defaults to True.
+ zero_angular_momentum (bool, optional): Whether to remove angular momentum from the system. Defaults to True.
+ traj_file (str | Path | None, optional): Path to the trajectory file for saving simulation results. Defaults to None.
+ traj_interval (int, optional): Interval for saving trajectory frames. Defaults to 1.
+ restart (bool, optional): Whether to restart the simulation from an existing trajectory file. Defaults to True.
+
+ Returns:
+ dict: A dictionary containing the following keys:
+ - "atoms" (Atoms): The final atomic structure after the simulation.
+ - "runtime" (timedelta): The runtime of the simulation.
+ - "n_steps" (int): The number of steps performed in the simulation.
+
+ Raises:
+ ValueError: If an invalid dynamics method is specified or if the dynamics method is incompatible with the ensemble.
+
+ Notes:
+ - The function supports restarting from an existing trajectory file if `restart` is True.
+ - For NPT dynamics, the atomic cell is transformed to an upper triangular form to meet ASE's requirements.
+ - Temperature and pressure schedules can be specified as sequences or arrays for time-dependent control.
+ """
+
+ atoms = atoms.copy()
+
+ atoms.calc = calculator
+
+ if time_step is None:
+ # If a structure contains an isotope of hydrogen, set default `time_step`
+ # to 0.5 fs, and 2 fs otherwise.
+ has_h_isotope = "H" in atoms.get_chemical_symbols()
+ time_step = 0.5 if has_h_isotope else 2.0
+
+ n_steps = int(total_time / time_step)
+ target_steps = n_steps
+
+ t_schedule, p_schedule = _get_ensemble_schedule(
+ ensemble=ensemble,
+ n_steps=n_steps,
+ temperature=temperature,
+ pressure=pressure,
+ )
+
+ dynamics_kwargs = _get_ensemble_defaults(
+ ensemble=ensemble,
+ dynamics=dynamics,
+ t_schedule=t_schedule,
+ p_schedule=p_schedule,
+ dynamics_kwargs=dynamics_kwargs,
+ )
+
+ if isinstance(dynamics, str):
+ # Use known dynamics if `self.dynamics` is a str
+ dynamics = dynamics.lower()
+ if dynamics not in _valid_dynamics[ensemble]:
+ raise ValueError(
+ f"{dynamics} thermostat not available for {ensemble}."
+ f"Available {ensemble} thermostats are:"
+ " ".join(_valid_dynamics[ensemble])
+ )
+ if ensemble == "nve":
+ dynamics = "velocityverlet"
+ md_class = _preset_dynamics[f"{ensemble}_{dynamics}"]
+ elif dynamics is MolecularDynamics:
+ md_class = dynamics
+ else:
+ raise ValueError(f"Invalid dynamics: {dynamics}")
+
+ if md_class is NPT:
+ # Note that until md_func is instantiated, isinstance(md_func,NPT) is False
+ # ASE NPT implementation requires upper triangular cell
+ u, _ = schur(atoms.get_cell(complete=True), output="complex")
+ atoms.set_cell(u.real, scale_atoms=True)
+
+ last_step = 0
+
+ if traj_file is not None:
+ traj_file = Path(traj_file)
+ traj_file.parent.mkdir(parents=True, exist_ok=True)
+
+ if restart and traj_file.exists():
+ try:
+ last_atoms = read(traj_file, index="-1")
+ assert isinstance(last_atoms, Atoms)
+ last_step = last_atoms.info.get("step")
+ n_steps -= last_step
+ traj = Trajectory(traj_file, "a", atoms)
+ atoms.set_positions(last_atoms.get_positions())
+ atoms.set_momenta(last_atoms.get_momenta())
+ except Exception:
+ traj = Trajectory(traj_file, "w", atoms)
+
+ if not np.isnan(t_schedule).any():
+ MaxwellBoltzmannDistribution(
+ atoms=atoms,
+ temperature_K=t_schedule[last_step],
+ rng=np.random.default_rng(seed=velocity_seed),
+ )
+
+ if zero_linear_momentum:
+ Stationary(atoms)
+ if zero_angular_momentum:
+ ZeroRotation(atoms)
+ else:
+ traj = Trajectory(traj_file, "w", atoms)
+
+ if not np.isnan(t_schedule).any():
+ MaxwellBoltzmannDistribution(
+ atoms=atoms,
+ temperature_K=t_schedule[last_step],
+ rng=np.random.default_rng(seed=velocity_seed),
+ )
+
+ if zero_linear_momentum:
+ Stationary(atoms)
+ if zero_angular_momentum:
+ ZeroRotation(atoms)
+
+ fraction_traceless = dynamics_kwargs.pop("fraction_traceless", 1.0)
+
+ md_runner = md_class(
+ atoms=atoms,
+ timestep=time_step * units.fs,
+ **dynamics_kwargs,
+ )
+ if md_class is NPT:
+ md_runner.set_fraction_traceless(fraction_traceless)
+
+ if traj_file is not None:
+ md_runner.attach(traj.write, interval=traj_interval)
+
+ with tqdm(total=n_steps) as pbar:
+
+ def _callback(dyn: MolecularDynamics = md_runner) -> None:
+ step = last_step + dyn.nsteps
+ dyn.atoms.info["restart"] = last_step
+ dyn.atoms.info["datetime"] = datetime.now()
+ dyn.atoms.info["step"] = step
+ dyn.atoms.info["target_steps"] = target_steps
+ if ensemble == "nve":
+ return
+ dyn.set_temperature(temperature_K=t_schedule[step])
+ if ensemble == "nvt":
+ return
+ dyn.set_stress(p_schedule[step])
+ pbar.update()
+
+ md_runner.attach(_callback, interval=1)
+
+ start_time = datetime.now()
+ md_runner.run(steps=n_steps)
+ end_time = datetime.now()
+
+ if traj_file is not None:
+ traj.close()
+
+ return {
+ "atoms": atoms,
+ "runtime": end_time - start_time,
+ "n_steps": n_steps,
+ }
diff --git a/mlip_arena/tasks/mof/LICENSE b/mlip_arena/tasks/mof/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..9bec9ea1dce149f68680f79422395ef547b5ce4e
--- /dev/null
+++ b/mlip_arena/tasks/mof/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Hyunsoo Park
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/mlip_arena/tasks/mof/__init__.py b/mlip_arena/tasks/mof/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e5966491b03815c17a1dcba6f30a3ebb937dcdd
--- /dev/null
+++ b/mlip_arena/tasks/mof/__init__.py
@@ -0,0 +1,17 @@
+from pathlib import Path
+from loguru import logger
+
+license_path = Path(__file__).parent / "LICENSE"
+
+logger.info(f"""
+The module '{__name__}' is adapted from the repository: https://github.com/hspark1212/DAC-SIM.
+By using this module, you agree to the terms and conditions specified in the following license:
+
+https://github.com/hspark1212/DAC-SIM/blob/main/LICENSE
+
+Additionally, please ensure proper attribution by citing the reference:
+
+Lim, Y., Park, H., Walsh, A., & Kim, J. (2024). Accelerating COโ Direct Air Capture Screening for Metal-Organic Frameworks with a Transferable Machine Learning Force Field.
+
+A local copy of the LICENSE file can be found at: {license_path}.
+""")
diff --git a/mlip_arena/tasks/mof/flow.py b/mlip_arena/tasks/mof/flow.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2f39e8b22b3628cd9a2c844aecc5cc6fe2fbf7d
--- /dev/null
+++ b/mlip_arena/tasks/mof/flow.py
@@ -0,0 +1,360 @@
+"""
+Widom insertion workflow to calculate Henry coefficient and heat of adsorption for a given MOF structure and gas molecule.
+
+
+This script is heavily adapted from the `DAC-SIM `_ package. Please cite the original work if you use this script.
+
+References
+~~~~~~~~~~~
+- Lim, Y., Park, H., Walsh, A., & Kim, J. (2024). Accelerating COโ Direct Air Capture Screening for Metal-Organic Frameworks with a Transferable Machine Learning Force Field.
+"""
+
+from collections import defaultdict
+from pathlib import Path
+from typing import IO, Any
+
+import numpy as np
+from prefect import flow, task
+from prefect.cache_policies import INPUTS, TASK_SOURCE
+from prefect.futures import wait
+from prefect.logging import get_run_logger
+from prefect.runtime import task_run
+from prefect.states import State
+from tqdm.auto import tqdm
+
+from ase import Atoms, units
+from ase.atoms import Atoms
+from ase.build import molecule
+from ase.filters import Filter
+from ase.io.trajectory import Trajectory, TrajectoryWriter
+from ase.optimize.optimize import Optimizer
+from ase.calculators.calculator import BaseCalculator
+from mlip_arena.models import MLIPEnum
+from mlip_arena.tasks.optimize import run as OPT
+from mlip_arena.tasks.utils import get_calculator, logger
+
+from .grid import get_accessible_positions
+from .input import get_atoms_from_db
+
+
+def add_molecule(gas: Atoms, rotate: bool = True, translate: tuple = None) -> Atoms:
+ """
+ Add a molecule to the simulation cell
+
+ Parameters
+ ----------
+ gas : Atoms
+ The gas molecule to add
+ rotate : bool, optional
+ If True, rotate the molecule randomly, by default True
+ translate : tuple, optional
+ The translation of the molecule, by default None
+
+ Returns
+ -------
+ Atoms
+ The gas molecule added to the simulation cell
+
+ Raises
+ ------
+ ValueError
+ If the translate is not a 3-tuple, raise an error
+
+ Examples
+ --------
+ >>> from ml_mc.utils import molecule, add_gas
+ >>> gas = molecule('H2O')
+ >>> gas = add_gas(gas, rotate=True, translate=(0, 0, 0))
+ """
+ gas = gas.copy()
+ if rotate:
+ angle = np.random.rand() * 360
+ axis = np.random.rand(3)
+ gas.rotate(v=axis, a=angle)
+ if translate is not None:
+ if len(translate) != 3:
+ raise ValueError("translate must be a 3-tuple")
+ gas.translate(translate)
+ return gas
+
+
+def get_atomic_density(atoms: Atoms) -> float:
+ """
+ Calculate atomic density of the atoms.
+
+ Parameters
+ ----------
+ atoms : Atoms
+ The Atoms object to operate on.
+
+ Returns
+ -------
+ float
+ Atomic density of the atoms in kg/mยณ.
+ """
+ volume = atoms.get_volume() * 1e-30 # Convert ร ยณ to mยณ
+ total_mass = sum(atoms.get_masses()) * units._amu # Convert amu to kg
+ return total_mass / volume
+
+
+def _generate_task_run_name():
+ task_name = task_run.task_name
+ parameters = task_run.parameters
+
+ structure = parameters["structure"]
+ gas = parameters["gas"]
+ calculator = parameters["calculator"]
+
+ return f"{task_name}: {structure.get_chemical_formula()} + {gas.get_chemical_formula()} - {calculator}"
+
+
+@task(
+ name="Widom Insertion",
+ task_run_name=_generate_task_run_name,
+ cache_policy=TASK_SOURCE + INPUTS,
+)
+def widom_insertion(
+ # init
+ structure: Atoms,
+ gas: Atoms,
+ calculator: BaseCalculator,
+ optimizer: Optimizer | str = "FIRE",
+ optimizer_kwargs: dict | None = None,
+ filter: Filter | str | None = "FrechetCell",
+ filter_kwargs: dict | None = None,
+ criterion: dict | None = dict(fmax=0.05, steps=50),
+ temperature: float = 300,
+ init_structure_optimize_loops: int = 10,
+ init_gas_optimize: bool = True,
+ traj_file: str | Path | None = None,
+ # run
+ num_insertions: int = 5000,
+ grid_spacing: float = 0.15,
+ cutoff_distance: float = 1.50,
+ min_interplanar_distance: float = 6.0,
+ fold: int = 3,
+ random_seed: int | None = None,
+) -> dict[str, Any] | State:
+ """
+ Run the Widom insertion algorithm to calculate the Henry coefficient and heat of adsorption.
+
+ Parameters
+ ----------
+ num_insertions : int, default=5000
+ Number of random insertions of the gas molecule during simulation.
+ grid_spacing : float, default=0.15
+ Spacing of the grid for possible gas insertion points, in angstroms.
+ cutoff_distance : float, default=1.50
+ When the distance between framework atoms and the gas molecule is less than this value, the insertion is rejected. In angstroms.
+ min_interplanar_distance : float, default=6.0
+ When the interplanar distance of the framework is less than this value, a supercell is constructed. In angstroms.
+ fold : int, default=3
+ Number of repetitions of Widom insertion to improve statistics.
+ random_seed : int, optional
+ Seed for the random number generator for reproducibility.
+
+ Returns
+ -------
+ Dict[str, Any]
+ Dictionary containing the calculated Henry coefficient (mol/kg Pa), averaged interaction energy (eV), and heat of adsorption (kJ/mol) over the number of folds.
+ """
+
+ structure = structure.copy()
+ gas = gas.copy()
+
+ # Optimize structure and gas molecule
+ while init_structure_optimize_loops > 0:
+ logger.info("Optimizing cell")
+ state = OPT(
+ atoms=structure,
+ calculator=calculator,
+ optimizer=optimizer,
+ optimizer_kwargs=optimizer_kwargs,
+ filter=filter,
+ filter_kwargs=filter_kwargs,
+ criterion=criterion,
+ return_state=True,
+ )
+
+ if state.is_failed():
+ return state
+
+ result = state.result(raise_on_failure=False)
+ structure = result["atoms"]
+ if result["converged"]:
+ break
+
+ logger.info("Optimizing atoms with fixed cell")
+ state = OPT(
+ atoms=structure,
+ calculator=calculator,
+ optimizer=optimizer,
+ optimizer_kwargs=optimizer_kwargs,
+ filter=None,
+ filter_kwargs=None,
+ criterion=criterion,
+ return_state=True,
+ )
+
+ if state.is_failed():
+ return state
+
+ result = state.result(raise_on_failure=False)
+ structure = result["atoms"]
+ if result["converged"]:
+ break
+
+ init_structure_optimize_loops -= 1
+
+ if init_gas_optimize:
+ logger.info("Optimizing gas molecule")
+ state = OPT(
+ atoms=gas,
+ calculator=calculator,
+ optimizer=optimizer,
+ optimizer_kwargs=optimizer_kwargs,
+ filter=None,
+ criterion=criterion,
+ return_state=True,
+ )
+
+ if state.is_failed():
+ return state
+
+ gas = state.result(raise_on_failure=False)["atoms"]
+
+ # Calculate accessible positions
+ ret = get_accessible_positions(
+ structure=structure,
+ grid_spacing=grid_spacing,
+ cutoff_distance=cutoff_distance,
+ min_interplanar_distance=min_interplanar_distance,
+ )
+ pos_grid = ret["pos_grid"]
+ idx_accessible_pos = ret["idx_accessible_pos"]
+ structure = ret["structure"] # supercell structure if necessary
+
+ logger.info(
+ f"Number of accessible positions: {len(idx_accessible_pos)} out of total {len(pos_grid)}"
+ )
+
+ calc = calculator
+ # Calculate energies for structure and gas
+ energy_structure = calc.get_potential_energy(structure)
+ energy_gas = calc.get_potential_energy(gas)
+
+ # Set random seed if provided
+ if random_seed is not None:
+ np.random.seed(random_seed)
+ logger.info(f"Setting random seed: {random_seed}")
+
+ if traj_file is not None:
+ traj_file = Path(traj_file)
+ traj_file.parent.mkdir(parents=True, exist_ok=True)
+ # TODO: checkpoint and restart
+ traj = Trajectory(traj_file, "a")
+ else:
+ traj = None
+
+ # Run Widom insertion algorithm
+
+ results = defaultdict(list)
+ for ifold in range(fold):
+
+ nsteps = 0
+
+ np.random.shuffle(idx_accessible_pos)
+ interaction_energies = np.zeros(num_insertions)
+
+ pbar = tqdm(total=num_insertions, desc=f"Fold {ifold + 1}/{fold}")
+ for rand_idx in idx_accessible_pos:
+ # assert rand_idx in idx_accessible_pos
+
+ if nsteps >= num_insertions:
+ break
+
+ # Add gas molecule to the accessible position
+ pos = pos_grid[rand_idx]
+ added_gas = add_molecule(gas, rotate=True, translate=pos)
+ structure_with_gas = structure + added_gas
+ structure_with_gas.wrap() # wrap atoms to unit cell
+
+ # Calculate interaction energy
+ structure_with_gas.calc = calc
+ total_energy = structure_with_gas.get_potential_energy() # [eV]
+ interaction_energy = total_energy - energy_structure - energy_gas # [eV]
+
+ boltzmann_factor = np.exp(
+ -interaction_energy / (temperature * units._k / units._e)
+ )
+
+ # Handle exponential overflow that can cause numerical instability
+
+ max_exp_arg = 700 # np.exp(700) is close to the max float64
+ if boltzmann_factor > np.exp(max_exp_arg):
+ logger.warning(
+ f"Exponential overflow detected. Rejecting this step and retrying."
+ )
+ continue
+
+ interaction_energies[nsteps] = interaction_energy
+ nsteps += 1
+ pbar.update(1)
+
+ # Write trajectory
+ if isinstance(traj, TrajectoryWriter):
+ traj.write(structure_with_gas)
+
+ pbar.close()
+
+ assert nsteps == num_insertions, "Cannot reach the number of insertions due to too many invalid steps."
+
+ # Calculate ensemble averages properties
+ # units._e [J/eV], units._k [J/K], units._k / units._e # [eV/K]
+ boltzmann_factors = np.exp(
+ -interaction_energies / (temperature * units._k / units._e)
+ )
+
+ # KH = / (R * T)
+ atomic_density = get_atomic_density(structure) # [kg / m^3]
+ kh = (
+ boltzmann_factors.sum()
+ / num_insertions
+ / (units._k * units._Nav) # R = [J / mol K] = [Pa m^3 / mol K]
+ / temperature # T = [K] -> [mol/ m^3 Pa]
+ / atomic_density # = [kg / m^3] -> [mol / kg Pa]
+ ) # [mol/kg Pa]
+
+ # U = < E * exp(-E/RT) > / # [eV]
+ u = (interaction_energies * boltzmann_factors).sum() / boltzmann_factors.sum()
+
+ # Qst = U - RT # [kJ/mol]
+ qst = (u * units._e - units._k * temperature) * units._Nav * 1e-3
+
+ results["henry_coefficient"].append(kh)
+ results["averaged_interaction_energy"].append(u)
+ results["heat_of_adsorption"].append(qst)
+
+ return results
+
+
+@flow
+def run(
+ db_path: Path | str = "mofs.db",
+):
+ states = []
+ for model in MLIPEnum:
+ for atoms in tqdm(get_atoms_from_db(db_path)):
+ state = widom_insertion.submit(
+ atoms,
+ molecule("CO2"),
+ calculator=get_calculator(
+ model,
+ dispersion=True,
+ ),
+ return_state=True,
+ )
+ states.append(state)
+
+ wait(states)
+ return [s.result(raise_on_failture=False) for s in states if s.is_completed()]
diff --git a/mlip_arena/tasks/mof/grid.py b/mlip_arena/tasks/mof/grid.py
new file mode 100644
index 0000000000000000000000000000000000000000..152f622004faf2572623f109c9eb1b3756f9308b
--- /dev/null
+++ b/mlip_arena/tasks/mof/grid.py
@@ -0,0 +1,60 @@
+"""
+Grid search for accessible positions
+
+
+This script is heavily adapted from the `DAC-SIM `_ package. Please cite the original work if you use this script.
+
+References
+~~~~~~~~~~~
+- Lim, Y., Park, H., Walsh, A., & Kim, J. (2024). Accelerating COโ Direct Air Capture Screening for Metal-Organic Frameworks with a Transferable Machine Learning Force Field.
+"""
+
+import MDAnalysis as mda
+import numpy as np
+
+from ase import Atoms
+
+
+def get_accessible_positions(
+ structure: Atoms,
+ grid_spacing: float = 0.5,
+ cutoff_distance: float = 10.0,
+ min_interplanar_distance: float = 2.0,
+) -> dict:
+ # get the supercell structure
+ cell_volume = structure.get_volume()
+ cell_vectors = np.array(structure.cell)
+ dist_a = cell_volume / np.linalg.norm(np.cross(cell_vectors[1], cell_vectors[2]))
+ dist_b = cell_volume / np.linalg.norm(np.cross(cell_vectors[2], cell_vectors[0]))
+ dist_c = cell_volume / np.linalg.norm(np.cross(cell_vectors[0], cell_vectors[1]))
+ plane_distances = np.array([dist_a, dist_b, dist_c])
+ supercell = np.ceil(min_interplanar_distance / plane_distances).astype(int)
+ if np.any(supercell > 1):
+ print(
+ f"Making supercell: {supercell} to prevent interplanar distance < {min_interplanar_distance}"
+ )
+ structure = structure.repeat(supercell)
+ # get position for grid
+ grid_size = np.ceil(np.array(structure.cell.cellpar()[:3]) / grid_spacing).astype(
+ int
+ )
+ indices = np.indices(grid_size).reshape(3, -1).T # (G, 3)
+ pos_grid = indices.dot(cell_vectors / grid_size) # (G, 3)
+ # get positions for atoms
+ pos_atoms = structure.get_positions() # (N, 3)
+ # distance matrix
+ dist_matrix = mda.lib.distances.distance_array(
+ pos_grid, pos_atoms, box=structure.cell.cellpar()
+ ) # (G, N) # TODO: check if we could use other packages instead of mda
+
+ # calculate the accessible positions
+ min_dist = np.min(dist_matrix, axis=1) # (G,)
+ idx_accessible_pos = np.where(min_dist > cutoff_distance)[0]
+
+ # result
+ return {
+ "pos_grid": pos_grid,
+ "idx_accessible_pos": idx_accessible_pos,
+ "accessible_pos": pos_grid[idx_accessible_pos],
+ "structure": structure,
+ }
diff --git a/mlip_arena/tasks/mof/input.py b/mlip_arena/tasks/mof/input.py
new file mode 100644
index 0000000000000000000000000000000000000000..89ef833f2dc31d7976c6829bf03db2a91aa80145
--- /dev/null
+++ b/mlip_arena/tasks/mof/input.py
@@ -0,0 +1,69 @@
+import os
+from pathlib import Path
+from typing import Generator, Iterable
+from loguru import logger
+from huggingface_hub import HfApi, hf_hub_download
+from prefect import task
+
+from ase import Atoms
+from ase.db import connect
+
+
+def save_to_db(
+ atoms_list: list[Atoms] | Iterable[Atoms] | Atoms,
+ db_path: Path | str,
+ upload: bool = True,
+ hf_token: str | None = os.getenv("HF_TOKEN", None),
+ repo_id: str = "atomind/mlip-arena",
+ repo_type: str = "dataset",
+ subfolder: str = Path(__file__).parent.name,
+):
+ """Save ASE Atoms objects to an ASE database and optionally upload to Hugging Face Hub."""
+
+ if upload and hf_token is None:
+ raise ValueError("HF_TOKEN is required to upload the database.")
+
+ db_path = Path(db_path)
+
+ if isinstance(atoms_list, Atoms):
+ atoms_list = [atoms_list]
+
+ with connect(db_path) as db:
+ for atoms in atoms_list:
+ if not isinstance(atoms, Atoms):
+ raise ValueError("atoms_list must contain ASE Atoms objects.")
+ db.write(atoms)
+
+ if upload:
+ api = HfApi(token=hf_token)
+ api.upload_file(
+ path_or_fileobj=db_path,
+ path_in_repo=f"{subfolder}/{db_path.name}",
+ repo_id=repo_id,
+ repo_type=repo_type,
+ )
+ logger.info(f"{db_path.name} uploaded to {repo_id}/{subfolder}")
+
+ return db_path
+
+@task
+def get_atoms_from_db(
+ db_path: Path | str,
+ hf_token: str | None = os.getenv("HF_TOKEN", None),
+ repo_id: str = "atomind/mlip-arena",
+ repo_type: str = "dataset",
+ subfolder: str = Path(__file__).parent.name,
+) -> Generator[Atoms, None, None]:
+ """Retrieve ASE Atoms objects from an ASE database."""
+ db_path = Path(db_path)
+ if not db_path.exists():
+ db_path = hf_hub_download(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ subfolder=subfolder,
+ filename=str(db_path),
+ token=hf_token,
+ )
+ with connect(db_path) as db:
+ for row in db.select():
+ yield row.toatoms()
diff --git a/mlip_arena/tasks/neb.py b/mlip_arena/tasks/neb.py
new file mode 100644
index 0000000000000000000000000000000000000000..523e17f0d72cb0e40e6e4248e5b1821ca5b3db75
--- /dev/null
+++ b/mlip_arena/tasks/neb.py
@@ -0,0 +1,248 @@
+"""
+Defines nudged elastic band (NEB) task
+
+This module has been modified from MatCalc
+https://github.com/materialsvirtuallab/matcalc/blob/main/src/matcalc/neb.py
+
+https://github.com/materialsvirtuallab/matcalc/blob/main/LICENSE
+
+BSD 3-Clause License
+
+Copyright (c) 2023, Materials Virtual Lab
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+ list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+ this list of conditions and the following disclaimer in the documentation
+ and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+ contributors may be used to endorse or promote products derived from
+ this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, Literal
+
+from ase import Atoms
+from ase.calculators.calculator import BaseCalculator
+from ase.filters import * # type: ignore
+from ase.mep.neb import NEB, NEBTools
+from ase.optimize import * # type: ignore
+from ase.optimize.optimize import Optimizer
+from ase.utils.forcecurve import fit_images
+from prefect import task
+from prefect.cache_policies import INPUTS, TASK_SOURCE
+from prefect.runtime import task_run
+from prefect.states import State
+
+from mlip_arena.tasks.optimize import run as OPT
+from mlip_arena.tasks.utils import logger, pformat
+from pymatgen.io.ase import AseAtomsAdaptor
+
+_valid_optimizers: dict[str, Optimizer] = {
+ "MDMin": MDMin,
+ "FIRE": FIRE,
+ "FIRE2": FIRE2,
+ "LBFGS": LBFGS,
+ "LBFGSLineSearch": LBFGSLineSearch,
+ "BFGS": BFGS,
+ # "BFGSLineSearch": BFGSLineSearch, # NEB does not support BFGSLineSearch
+ "QuasiNewton": QuasiNewton,
+ "GPMin": GPMin,
+ "CellAwareBFGS": CellAwareBFGS,
+ "ODE12r": ODE12r,
+} # type: ignore
+
+
+def _generate_task_run_name():
+ task_name = task_run.task_name
+ parameters = task_run.parameters
+
+ if "images" in parameters:
+ atoms = parameters["images"][0]
+ elif "start" in parameters:
+ atoms = parameters["start"]
+ else:
+ raise ValueError("No images or start atoms found in parameters")
+
+ calculator_name = parameters["calculator"]
+
+ return f"{task_name}: {atoms.get_chemical_formula()} - {calculator_name}"
+
+
+@task(
+ name="NEB from images",
+ task_run_name=_generate_task_run_name,
+ cache_policy=TASK_SOURCE + INPUTS,
+)
+def run(
+ images: list[Atoms],
+ calculator: BaseCalculator,
+ optimizer: Optimizer | str = "MDMin", # type: ignore
+ optimizer_kwargs: dict | None = None,
+ criterion: dict | None = None,
+ interpolation: Literal["linear", "idpp"] = "idpp",
+ climb: bool = True,
+ traj_file: str | Path | None = None,
+) -> dict[str, Any] | State:
+ """Run the nudged elastic band (NEB) calculation.
+
+ Args:
+ images (list[Atoms]): The images.
+ calculator_name (str | MLIPEnum): The calculator name.
+ calculator_kwargs (dict, optional): The calculator kwargs. Defaults to None.
+ dispersion (str, optional): The dispersion. Defaults to None.
+ dispersion_kwargs (dict, optional): The dispersion kwargs. Defaults to None.
+ device (str, optional): The device. Defaults to None.
+ optimizer (Optimizer | str, optional): The optimizer. Defaults to "BFGSLineSearch".
+ optimizer_kwargs (dict, optional): The optimizer kwargs. Defaults to None.
+ criterion (dict, optional): The criterion. Defaults to None.
+ interpolation (Literal['linear', 'idpp'], optional): The interpolation method. Defaults to "idpp".
+ climb (bool, optional): Whether to use the climbing image. Defaults to True.
+ traj_file (str | Path, optional): The trajectory file. Defaults to None.
+
+ Returns:
+ dict[str, Any] | State: The energy barrier.
+ """
+
+ images = [image.copy() for image in images]
+
+ for image in images:
+ assert isinstance(image, Atoms)
+ image.calc = calculator
+
+ neb = NEB(images, climb=climb, allow_shared_calculator=True)
+
+ neb.interpolate(method=interpolation)
+
+ if isinstance(optimizer, str):
+ if optimizer not in _valid_optimizers:
+ raise ValueError(f"Invalid optimizer: {optimizer}")
+ optimizer = _valid_optimizers[optimizer]
+
+ optimizer_kwargs = optimizer_kwargs or {}
+ criterion = criterion or {}
+
+ optimizer_instance = optimizer(neb, trajectory=traj_file, **optimizer_kwargs) # type: ignore
+ logger.info(f"Using optimizer: {optimizer_instance}")
+ logger.info(pformat(optimizer_kwargs))
+ logger.info(f"Criterion: {pformat(criterion)}")
+ optimizer_instance.run(**criterion)
+
+ neb_tool = NEBTools(neb.images)
+
+ return {
+ "barrier": neb_tool.get_barrier(),
+ "images": neb.images,
+ "forcefit": fit_images(neb.images),
+ }
+
+
+@task(
+ name="NEB from endpoints",
+ task_run_name=_generate_task_run_name,
+ cache_policy=TASK_SOURCE + INPUTS,
+)
+def run_from_endpoints(
+ start: Atoms,
+ end: Atoms,
+ n_images: int,
+ calculator: BaseCalculator,
+ optimizer: Optimizer | str = "BFGS", # type: ignore
+ optimizer_kwargs: dict | None = None,
+ criterion: dict | None = None,
+ relax_end_points: bool = True,
+ interpolation: Literal["linear", "idpp"] = "idpp",
+ climb: bool = True,
+ traj_file: str | Path | None = None,
+ cache_subtasks: bool = False,
+) -> dict[str, Any] | State:
+ """Run the nudged elastic band (NEB) calculation from end points.
+
+ Args:
+ start (Atoms): The start image.
+ end (Atoms): The end image.
+ n_images (int): The number of images.
+ calculator_name (str | MLIPEnum): The calculator name.
+ calculator_kwargs (dict, optional): The calculator kwargs. Defaults to None.
+ dispersion (str, optional): The dispersion. Defaults to None.
+ dispersion_kwargs (dict, optional): The dispersion kwargs. Defaults to None.
+ device (str, optional): The device. Defaults to None.
+ optimizer (Optimizer | str, optional): The optimizer. Defaults to "BFGSLineSearch".
+ optimizer_kwargs (dict, optional): The optimizer kwargs. Defaults to None.
+ criterion (dict, optional): The criterion. Defaults to None.
+ interpolation (Literal['linear', 'idpp'], optional): The interpolation method. Defaults to "idpp".
+ climb (bool, optional): Whether to use the climbing image. Defaults to True.
+ traj_file (str | Path, optional): The trajectory file. Defaults to None.
+
+ Returns:
+ dict[str, Any] | State: The energy barrier.
+ """
+
+ if relax_end_points:
+ relax = OPT.with_options(
+ refresh_cache=not cache_subtasks,
+ )(
+ atoms=start.copy(),
+ calculator=calculator,
+ optimizer=optimizer,
+ optimizer_kwargs=optimizer_kwargs,
+ criterion=criterion,
+ )
+ start = relax["atoms"]
+
+ relax = OPT.with_options(
+ refresh_cache=not cache_subtasks,
+ )(
+ atoms=end.copy(),
+ calculator=calculator,
+ optimizer=optimizer,
+ optimizer_kwargs=optimizer_kwargs,
+ criterion=criterion,
+ )
+ end = relax["atoms"]
+
+ path = (
+ AseAtomsAdaptor()
+ .get_structure(start)
+ .interpolate(
+ AseAtomsAdaptor().get_structure(end),
+ nimages=n_images - 1,
+ interpolate_lattices=False,
+ pbc=False,
+ autosort_tol=0.5,
+ )
+ )
+
+ images = [s.to_ase_atoms(msonable=False) for s in path]
+
+ return run.with_options(
+ refresh_cache=not cache_subtasks,
+ )(
+ images,
+ calculator=calculator,
+ optimizer=optimizer,
+ optimizer_kwargs=optimizer_kwargs,
+ criterion=criterion,
+ interpolation=interpolation,
+ climb=climb,
+ traj_file=traj_file,
+ )
diff --git a/mlip_arena/tasks/optimize.py b/mlip_arena/tasks/optimize.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0d5f478b0b055e3ae8598460000e24ad52ce127
--- /dev/null
+++ b/mlip_arena/tasks/optimize.py
@@ -0,0 +1,109 @@
+"""
+Define structure optimization tasks.
+"""
+
+from __future__ import annotations
+
+from ase import Atoms
+from ase.calculators.calculator import BaseCalculator
+from ase.constraints import FixSymmetry
+from ase.filters import * # type: ignore
+from ase.filters import Filter
+from ase.optimize import * # type: ignore
+from ase.optimize.optimize import Optimizer
+from prefect import task
+from prefect.cache_policies import INPUTS, TASK_SOURCE
+from prefect.runtime import task_run
+
+from mlip_arena.tasks.utils import logger, pformat
+
+_valid_filters: dict[str, Filter] = {
+ "Filter": Filter,
+ "UnitCell": UnitCellFilter,
+ "ExpCell": ExpCellFilter,
+ "Strain": StrainFilter,
+ "FrechetCell": FrechetCellFilter,
+} # type: ignore
+
+_valid_optimizers: dict[str, Optimizer] = {
+ "MDMin": MDMin,
+ "FIRE": FIRE,
+ "FIRE2": FIRE2,
+ "LBFGS": LBFGS,
+ "LBFGSLineSearch": LBFGSLineSearch,
+ "BFGS": BFGS,
+ "BFGSLineSearch": BFGSLineSearch,
+ "QuasiNewton": QuasiNewton,
+ "GPMin": GPMin,
+ "CellAwareBFGS": CellAwareBFGS,
+ "ODE12r": ODE12r,
+} # type: ignore
+
+
+def _generate_task_run_name():
+ task_name = task_run.task_name
+ parameters = task_run.parameters
+
+ atoms = parameters["atoms"]
+ calculator_name = parameters["calculator"]
+
+ return f"{task_name}: {atoms.get_chemical_formula()} - {calculator_name}"
+
+
+@task(
+ name="OPT", task_run_name=_generate_task_run_name, cache_policy=TASK_SOURCE + INPUTS
+)
+def run(
+ atoms: Atoms,
+ calculator: BaseCalculator,
+ optimizer: Optimizer | str = BFGSLineSearch,
+ optimizer_kwargs: dict | None = None,
+ filter: Filter | str | None = None,
+ filter_kwargs: dict | None = None,
+ criterion: dict | None = None,
+ symmetry: bool = False,
+):
+ atoms = atoms.copy()
+ atoms.calc = calculator
+
+ if isinstance(filter, str):
+ if filter not in _valid_filters:
+ raise ValueError(f"Invalid filter: {filter}")
+ filter = _valid_filters[filter]
+
+ if isinstance(optimizer, str):
+ if optimizer not in _valid_optimizers:
+ raise ValueError(f"Invalid optimizer: {optimizer}")
+ optimizer = _valid_optimizers[optimizer]
+
+ filter_kwargs = filter_kwargs or {}
+ optimizer_kwargs = optimizer_kwargs or {}
+ criterion = criterion or dict(steps=1000)
+
+ if symmetry:
+ atoms.set_constraint(FixSymmetry(atoms))
+
+ if isinstance(filter, type) and issubclass(filter, Filter):
+ filter_instance = filter(atoms, **filter_kwargs)
+ logger.info(f"Using filter: {filter_instance}")
+ logger.info(pformat(filter_kwargs))
+
+ optimizer_instance = optimizer(filter_instance, **optimizer_kwargs)
+ logger.info(f"Using optimizer: {optimizer_instance}")
+ logger.info(pformat(optimizer_kwargs))
+ logger.info(f"Criterion: {pformat(criterion)}")
+
+ optimizer_instance.run(**criterion)
+ elif filter is None:
+ optimizer_instance = optimizer(atoms, **optimizer_kwargs)
+ logger.info(f"Using optimizer: {optimizer_instance}")
+ logger.info(pformat(optimizer_kwargs))
+ logger.info(f"Criterion: {pformat(criterion)}")
+ optimizer_instance.run(**criterion)
+
+
+ return {
+ "atoms": atoms,
+ "steps": optimizer_instance.nsteps,
+ "converged": optimizer_instance.converged(),
+ }
diff --git a/mlip_arena/tasks/phonon.py b/mlip_arena/tasks/phonon.py
new file mode 100644
index 0000000000000000000000000000000000000000..22b2638db5ae9c2dd5f740b3477a9e8e1fa8508e
--- /dev/null
+++ b/mlip_arena/tasks/phonon.py
@@ -0,0 +1,166 @@
+"""
+This module has been adapted from Quacc (https://github.com/Quantum-Accelerators/quacc). By using this software, you agree to the Quacc license agreement: https://github.com/Quantum-Accelerators/quacc/blob/main/LICENSE.md
+
+
+BSD 3-Clause License
+
+Copyright (c) 2025, Andrew S. Rosen.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+- Redistributions of source code must retain the above copyright notice, this
+ list of conditions and the following disclaimer.
+
+- Redistributions in binary form must reproduce the above copyright notice,
+ this list of conditions and the following disclaimer in the documentation
+ and/or other materials provided with the distribution.
+
+- Neither the name of the copyright holder nor the names of its
+ contributors may be used to endorse or promote products derived from
+ this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+from pathlib import Path
+
+import numpy as np
+from ase import Atoms
+from ase.calculators.calculator import BaseCalculator
+from prefect import task
+from prefect.cache_policies import INPUTS, TASK_SOURCE
+from prefect.runtime import task_run
+
+from mlip_arena.tasks.utils import logger
+
+try:
+ from phonopy import Phonopy
+ from phonopy.structure.atoms import PhonopyAtoms
+except ImportError as e:
+ logger.warning(e)
+ logger.warning(
+ "Phonopy is not installed. Please install it following the instructions at https://phonopy.github.io/phonopy/install.html to use this module."
+ )
+
+
+@task(cache_policy=TASK_SOURCE + INPUTS)
+def get_phonopy(
+ atoms: Atoms,
+ supercell_matrix: list[int] | None = None,
+ min_lengths: float | tuple[float, float, float] | None = None,
+ symprec: float = 1e-5,
+ distance: float = 0.01,
+ phonopy_kwargs: dict = {},
+) -> Phonopy:
+ if supercell_matrix is None and min_lengths is not None:
+ supercell_matrix = np.diag(
+ np.round(np.ceil(min_lengths / atoms.cell.lengths()))
+ )
+
+ phonon = Phonopy(
+ PhonopyAtoms(
+ symbols=atoms.get_chemical_symbols(),
+ cell=atoms.get_cell(),
+ scaled_positions=atoms.get_scaled_positions(wrap=True),
+ masses=atoms.get_masses(),
+ ),
+ symprec=symprec,
+ supercell_matrix=supercell_matrix,
+ **phonopy_kwargs,
+ )
+ phonon.generate_displacements(distance=distance)
+
+ return phonon
+
+
+def _get_forces(
+ phononpy_atoms: PhonopyAtoms,
+ calculator: BaseCalculator,
+) -> np.ndarray:
+ atoms = Atoms(
+ symbols=phononpy_atoms.symbols,
+ cell=phononpy_atoms.cell,
+ scaled_positions=phononpy_atoms.scaled_positions,
+ pbc=True,
+ )
+
+ atoms.calc = calculator
+
+ return atoms.get_forces()
+
+
+def _generate_task_run_name():
+ task_name = task_run.task_name
+ parameters = task_run.parameters
+
+ atoms = parameters["atoms"]
+ calculator_name = parameters["calculator"]
+
+ return f"{task_name}: {atoms.get_chemical_formula()} - {calculator_name}"
+
+
+@task(
+ name="PHONON",
+ task_run_name=_generate_task_run_name,
+ cache_policy=TASK_SOURCE + INPUTS,
+)
+def run(
+ atoms: Atoms,
+ calculator: BaseCalculator,
+ supercell_matrix: list[int] | None = None,
+ min_lengths: float | tuple[float, float, float] | None = None,
+ symprec: float = 1e-5,
+ distance: float = 0.01,
+ phonopy_kwargs: dict = {},
+ symmetry: bool = False,
+ t_min: float = 0.0,
+ t_max: float = 1000.0,
+ t_step: float = 10.0,
+ outdir: str | None = None,
+):
+ phonon = get_phonopy(
+ atoms=atoms.copy(),
+ supercell_matrix=supercell_matrix,
+ min_lengths=min_lengths,
+ symprec=symprec,
+ distance=distance,
+ phonopy_kwargs=phonopy_kwargs,
+ )
+
+ supercells_with_displacements = phonon.supercells_with_displacements
+
+ phonon.forces = [
+ _get_forces(supercell, calculator)
+ for supercell in supercells_with_displacements
+ if supercell is not None
+ ]
+ phonon.produce_force_constants()
+
+ if symmetry:
+ phonon.symmetrize_force_constants()
+ phonon.symmetrize_force_constants_by_space_group()
+
+ phonon.run_mesh(with_eigenvectors=True)
+ phonon.run_total_dos()
+ phonon.run_thermal_properties(t_step=t_step, t_max=t_max, t_min=t_min) # type: ignore
+ phonon.auto_band_structure(
+ write_yaml=True if outdir is not None else False,
+ filename=Path(outdir, "band.yaml") if outdir is not None else "band.yaml",
+ )
+ if outdir:
+ phonon.save(Path(outdir, "phonopy.yaml"), settings={"force_constants": True})
+
+ return {
+ "phonon": phonon,
+ }
diff --git a/mlip_arena/tasks/registry.yaml b/mlip_arena/tasks/registry.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..64f711e9d4cb915e9477248c82e987f7d02e6ca9
--- /dev/null
+++ b/mlip_arena/tasks/registry.yaml
@@ -0,0 +1,38 @@
+Homonuclear diatomics:
+ category: Fundamentals
+ task-page: homonuclear-diatomics
+ task-layout: wide
+ rank-page: homonuclear-diatomics
+ last-update: 2024-09-19
+Energy-volume scans:
+ category: Fundamentals
+ task-page: wbm_ev
+ task-layout: wide
+ rank-page: wbm_ev
+ last-update: 2025-04-29
+Equation of state:
+ category: Fundamentals
+ task-page: eos_bulk
+ task-layout: wide
+ rank-page: eos_bulk
+ last-update: 2025-04-29
+Combustion:
+ category: Molecular Dynamics
+ task-page: combustion
+ task-layout: centered
+ rank-page: combustion
+High pressure stability:
+ category: Molecular Dynamics
+ task-page: stability
+ task-layout: centered
+ rank-page:
+Lattice thermal conductivity:
+ category: Properties and Physical Behaviors
+ task-page: thermal-conductivity
+ task-layout: centered
+ rank-page: thermal-conductivity
+# 2D materials:
+# category: Properties and Physical Behaviors
+# task-page: c2db
+# task-layout: centered
+# rank-page:
diff --git a/mlip_arena/tasks/stability/__init__.py b/mlip_arena/tasks/stability/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mlip_arena/tasks/stability/chgnet/chloride-salts.json b/mlip_arena/tasks/stability/chgnet/chloride-salts.json
new file mode 100644
index 0000000000000000000000000000000000000000..4af31c3ed97e9463b52c541fec25cd5a9958f8bc
--- /dev/null
+++ b/mlip_arena/tasks/stability/chgnet/chloride-salts.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c29313314759696c662a7019bf9d18816e7370e3e8630fd2df2923e967b98868
+size 62991
diff --git a/mlip_arena/tasks/stability/input.py b/mlip_arena/tasks/stability/input.py
new file mode 100644
index 0000000000000000000000000000000000000000..11c47aced0c28b2b265924cbb6db460b8ee9fc22
--- /dev/null
+++ b/mlip_arena/tasks/stability/input.py
@@ -0,0 +1,74 @@
+import os
+from pathlib import Path
+from typing import Generator, Iterable
+
+from huggingface_hub import HfApi, hf_hub_download
+from prefect import task
+
+from ase import Atoms
+from ase.db import connect
+from mlip_arena.tasks.utils import logger
+
+
+def save_to_db(
+ atoms_list: list[Atoms] | Iterable[Atoms] | Atoms,
+ db_path: Path | str,
+ upload: bool = True,
+ hf_token: str | None = os.getenv("HF_TOKEN", None),
+ repo_id: str = "atomind/mlip-arena",
+ repo_type: str = "dataset",
+ subfolder: str = Path(__file__).parent.name,
+):
+ """Save ASE Atoms objects to an ASE database and optionally upload to Hugging Face Hub."""
+
+ if upload and hf_token is None:
+ raise ValueError("HF_TOKEN is required to upload the database.")
+
+ db_path = Path(db_path)
+
+ if isinstance(atoms_list, Atoms):
+ atoms_list = [atoms_list]
+
+ with connect(db_path) as db:
+ for atoms in atoms_list:
+ if not isinstance(atoms, Atoms):
+ raise ValueError("atoms_list must contain ASE Atoms objects.")
+ db.write(atoms)
+
+ if upload:
+ api = HfApi(token=hf_token)
+ api.upload_file(
+ path_or_fileobj=db_path,
+ path_in_repo=f"{subfolder}/{db_path.name}",
+ repo_id=repo_id,
+ repo_type=repo_type,
+ )
+ logger.info(f"{db_path.name} uploaded to {repo_id}/{subfolder}")
+
+ return db_path
+
+
+@task
+def get_atoms_from_db(
+ db_path: Path | str,
+ hf_token: str | None = os.getenv("HF_TOKEN", None),
+ repo_id: str = "atomind/mlip-arena",
+ repo_type: str = "dataset",
+ subfolder: str = Path(__file__).parent.name,
+ force_download: bool = False,
+) -> Generator[Atoms, None, None]:
+ """Retrieve ASE Atoms objects from an ASE database."""
+ db_path = Path(db_path)
+ if not db_path.exists():
+ db_path = hf_hub_download(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ subfolder=subfolder,
+ # local_dir=db_path.parent,
+ filename=db_path.name,
+ token=hf_token,
+ force_download=force_download,
+ )
+ with connect(db_path) as db:
+ for row in db.select():
+ yield row.toatoms()
diff --git a/mlip_arena/tasks/stability/mace-mp/chloride-salts.json b/mlip_arena/tasks/stability/mace-mp/chloride-salts.json
new file mode 100644
index 0000000000000000000000000000000000000000..d798ea26d7c44e2852cc28a9007e50ba82f1d087
--- /dev/null
+++ b/mlip_arena/tasks/stability/mace-mp/chloride-salts.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1788007f0728bd177466349a67812073a7d5e54b4fd45184aa53e5237ee5536f
+size 166882641
diff --git a/mlip_arena/tasks/stability/orb/chloride-salts.json b/mlip_arena/tasks/stability/orb/chloride-salts.json
new file mode 100644
index 0000000000000000000000000000000000000000..4fcdd5f86d0d4320a3fc835d9b83fdbf6ec48387
--- /dev/null
+++ b/mlip_arena/tasks/stability/orb/chloride-salts.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0a63ab2e52e104194dea43ffa456377bd355ef82b7b4a111456b81aeb054019
+size 12239582
diff --git a/mlip_arena/tasks/stability/sevennet/chloride-salts.json b/mlip_arena/tasks/stability/sevennet/chloride-salts.json
new file mode 100644
index 0000000000000000000000000000000000000000..ba8848c0237f98d6b982c1ea9571de9a195c4ed1
--- /dev/null
+++ b/mlip_arena/tasks/stability/sevennet/chloride-salts.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77e18217178cb40cfd8ba75cf4e32ec5586d8766fe7dc5268e665d5f51d6d4f4
+size 274466898
diff --git a/mlip_arena/tasks/thermal-conductivity/wte.csv b/mlip_arena/tasks/thermal-conductivity/wte.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ba74ba57b89772505916a0ab29f66ec55b985bc6
--- /dev/null
+++ b/mlip_arena/tasks/thermal-conductivity/wte.csv
@@ -0,0 +1,8 @@
+method,srme
+M3GNet,1.142
+CHGNet,1.717
+MACE-MP(M),0.647
+SevenNet,0.767
+ORBv2,1.732
+ORBv2(MPTrj),1.725
+eqV2(OMat-S),1.772
diff --git a/mlip_arena/tasks/utils.py b/mlip_arena/tasks/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7368b9ab63ba82a3a9f6b45aa25479871cce45b3
--- /dev/null
+++ b/mlip_arena/tasks/utils.py
@@ -0,0 +1,122 @@
+"""Utility functions for MLIP models."""
+
+from __future__ import annotations
+
+from pprint import pformat
+
+import torch
+from ase import units
+from ase.calculators.calculator import BaseCalculator
+from ase.calculators.mixing import SumCalculator
+
+from mlip_arena.models import MLIPEnum
+
+try:
+ from prefect.logging import get_run_logger
+
+ logger = get_run_logger()
+except (ImportError, RuntimeError):
+ from loguru import logger
+
+
+def get_freer_device() -> torch.device:
+ """Get the GPU with the most free memory, or use MPS if available.
+ s
+ Returns:
+ torch.device: The selected GPU device or MPS.
+
+ Raises:
+ ValueError: If no GPU or MPS is available.
+ """
+ device_count = torch.cuda.device_count()
+ if device_count > 0:
+ # If CUDA GPUs are available, select the one with the most free memory
+ mem_free = [
+ torch.cuda.get_device_properties(i).total_memory
+ - torch.cuda.memory_allocated(i)
+ for i in range(device_count)
+ ]
+ free_gpu_index = mem_free.index(max(mem_free))
+ device = torch.device(f"cuda:{free_gpu_index}")
+ logger.info(
+ f"Selected GPU {device} with {mem_free[free_gpu_index] / 1024**2:.2f} MB free memory from {device_count} GPUs"
+ )
+ elif torch.backends.mps.is_available():
+ # If no CUDA GPUs are available but MPS is, use MPS
+ logger.info("No GPU available. Using MPS.")
+ device = torch.device("mps")
+ else:
+ # Fallback to CPU if neither CUDA GPUs nor MPS are available
+ logger.info("No GPU or MPS available. Using CPU.")
+ device = torch.device("cpu")
+
+ return device
+
+
+def get_calculator(
+ calculator_name: str | MLIPEnum | BaseCalculator,
+ calculator_kwargs: dict | None = None,
+ dispersion: bool = False,
+ dispersion_kwargs: dict | None = None,
+ device: str | None = None,
+) -> BaseCalculator:
+ """Get a calculator with optional dispersion correction."""
+
+ device = device or str(get_freer_device())
+
+ calculator_kwargs = calculator_kwargs or {}
+ calculator_kwargs.update({"device": device})
+
+ logger.info(f"Using device: {device}")
+
+ if isinstance(calculator_name, MLIPEnum) and calculator_name in MLIPEnum:
+ calc = calculator_name.value(**calculator_kwargs)
+ calc.__str__ = lambda: calculator_name.name
+ elif isinstance(calculator_name, str) and hasattr(MLIPEnum, calculator_name):
+ calc = MLIPEnum[calculator_name].value(**calculator_kwargs)
+ calc.__str__ = lambda: calculator_name
+ elif isinstance(calculator_name, type) and issubclass(
+ calculator_name, BaseCalculator
+ ):
+ logger.warning(f"Using custom calculator class: {calculator_name}")
+ calc = calculator_name(**calculator_kwargs)
+ calc.__str__ = lambda: f"{calc.__class__.__name__}"
+ elif isinstance(calculator_name, BaseCalculator):
+ logger.warning(
+ f"Using custom calculator object (kwargs are ignored): {calculator_name}"
+ )
+ calc = calculator_name
+ calc.__str__ = lambda: f"{calc.__class__.__name__}"
+ else:
+ raise ValueError(f"Invalid calculator: {calculator_name}")
+
+ logger.info(f"Using calculator: {calc}")
+ if calculator_kwargs:
+ logger.info(pformat(calculator_kwargs))
+
+ dispersion_kwargs = dispersion_kwargs or dict(
+ damping="bj", xc="pbe", cutoff=40.0 * units.Bohr
+ )
+
+ dispersion_kwargs.update({"device": device})
+
+ if dispersion:
+ try:
+ from torch_dftd.torch_dftd3_calculator import TorchDFTD3Calculator
+ except ImportError as e:
+ raise ImportError(
+ "torch_dftd is required for dispersion but is not installed."
+ ) from e
+
+ disp_calc = TorchDFTD3Calculator(
+ **dispersion_kwargs,
+ )
+ calc = SumCalculator([calc, disp_calc])
+ # TODO: rename the SumCalculator
+
+ logger.info(f"Using dispersion: {disp_calc}")
+ if dispersion_kwargs:
+ logger.info(pformat(dispersion_kwargs))
+
+ assert isinstance(calc, BaseCalculator)
+ return calc
diff --git a/mlip_arena/tasks/vacancy_migration/__init__.py b/mlip_arena/tasks/vacancy_migration/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mlip_arena/tasks/vacancy_migration/input.py b/mlip_arena/tasks/vacancy_migration/input.py
new file mode 100644
index 0000000000000000000000000000000000000000..211bf8960c58d1d1af8ec48d9fbf7c7e92f01ed6
--- /dev/null
+++ b/mlip_arena/tasks/vacancy_migration/input.py
@@ -0,0 +1,192 @@
+from mp_api.client import MPRester
+
+from ase import Atom
+from ase.data import covalent_radii
+from ase.spacegroup import crystal
+
+fcc_elements = [
+ "Ac",
+ "Ag",
+ "Al",
+ "Ar",
+ "Au",
+ "Ba",
+ "Be",
+ "Ca",
+ "Cd",
+ "Ce",
+ "Co",
+ "Cs",
+ "Cu",
+ "Dy",
+ "Er",
+ "Fe",
+ "Ga",
+ "Ge",
+ "He",
+ "Hf",
+ "Ho",
+ "In",
+ "Ir",
+ "K",
+ "Kr",
+ "La",
+ "Li",
+ "Mg",
+ "Mn",
+ "Na",
+ "Ni",
+ "Os",
+ "Pa",
+ "Pb",
+ "Pd",
+ "Pr",
+ "Pt",
+ "Rb",
+ "Re",
+ "Rh",
+ "Ru",
+ "Sc",
+ "Sn",
+ "Sr",
+ "Ta",
+ "Tb",
+ "Tc",
+ "Th",
+ "Ti",
+ "Tl",
+ "W",
+ "Xe",
+ "Y",
+ "Zr"
+]
+
+def get_fcc_pristine(mp_api_key = None):
+ for element in fcc_elements:
+ with MPRester(mp_api_key) as mpr:
+ docs = mpr.materials.summary.search(
+ formula=element, spacegroup_number=225, fields=["structure", "energy_above_hull"]
+ )
+
+ docs = sorted(docs, key=lambda x: x.energy_above_hull)
+
+ if len(docs) != 0:
+ pristine = docs[0].structure.to_conventional().to_ase_atoms(msonable=False) * (3, 3, 3)
+
+ if len(pristine) != 108:
+ v = pristine.get_volume() / len(pristine)
+ r = v**(1/3)
+ a = 2*(2**0.5)*r
+
+ pristine = crystal(
+ symbols=[element]*4,
+ basis=[(0, 0, 0), (0.5, 0.5, 0), (0.5, 0, 0.5), (0, 0.5, 0.5)],
+ spacegroup=225,
+ cellpar=[a, a, a, 90, 90, 90],
+ ) * (3, 3, 3)
+ else:
+ r = covalent_radii[Atom(element).number] or 4
+ a = 2*(2**0.5)*r
+ pristine = crystal(
+ symbols=[element]*4,
+ basis=[(0, 0, 0), (0.5, 0.5, 0), (0.5, 0, 0.5), (0, 0.5, 0.5)],
+ spacegroup=225,
+ cellpar=[a, a, a, 90, 90, 90],
+ ) * (3, 3, 3)
+ yield pristine
+
+hcp_elements = [
+ "Ag",
+ "Al",
+ "Ar",
+ "Au",
+ "Ba",
+ "Be",
+ "Ca",
+ "Cd",
+ "Ce",
+ "Co",
+ "Cr",
+ "Cs",
+ "Cu",
+ "Fe",
+ "Ga",
+ "Ge",
+ "He",
+ "Hf",
+ "Ho",
+ "In",
+ "Ir",
+ "K",
+ "Kr",
+ "La",
+ "Li",
+ "Mg",
+ "Mn",
+ "Mo",
+ "Nb",
+ "Ne",
+ "Ni",
+ "Os",
+ "P",
+ "Pb",
+ "Pd",
+ "Pt",
+ "Rb",
+ "Re",
+ "Rh",
+ "Ru",
+ "Sc",
+ "Si",
+ "Sn",
+ "Sr",
+ "Ta",
+ "Tc",
+ "Te",
+ "Th",
+ "Ti",
+ "Tl",
+ "V",
+ "W",
+ "Xe",
+ "Y",
+ "Zn",
+ "Zr"
+]
+
+def get_hcp_pristine(mp_api_key = None):
+ for element in hcp_elements:
+ with MPRester(mp_api_key) as mpr:
+ docs = mpr.materials.summary.search(
+ formula=element, spacegroup_number=194, fields=["structure", "energy_above_hull"]
+ )
+
+ docs = sorted(docs, key=lambda x: x.energy_above_hull)
+
+ if len(docs) != 0:
+ pristine = docs[0].structure.to_conventional().to_ase_atoms(msonable=False) * (3, 3, 1)
+
+ if len(pristine) != 36:
+ v = pristine.get_volume() / len(pristine)
+ r = v**(1/3)
+ a = 2*r
+ c = 4 * ((2/3) ** 0.5) * r
+
+ pristine = crystal(
+ [element],
+ [(1.0 / 3.0, 2.0 / 3.0, 3.0 / 4.0)],
+ spacegroup=194,
+ cellpar=[a, a, c, 90, 90, 120],
+ ) * (3, 3, 2)
+ else:
+ r = covalent_radii[Atom(element).number] or 4
+ a = 2*r
+ c = 4 * ((2/3) ** 0.5) * r
+
+ pristine = crystal(
+ [element],
+ [(1.0 / 3.0, 2.0 / 3.0, 3.0 / 4.0)],
+ spacegroup=194,
+ cellpar=[a, a, c, 90, 90, 120],
+ ) * (3, 3, 2)
+ yield pristine
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..f1aa30552a3b467329053e75a6c9b72e99d02322
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,178 @@
+[build-system]
+requires=["flit_core >=3.2,<4"]
+build-backend="flit_core.buildapi"
+
+[project]
+name="mlip-arena"
+version="0.1.2"
+authors=[
+ {name="Yuan Chiang", email="cyrusyc@lbl.gov"},
+]
+description="Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond error-based regression metrics"
+readme=".github/README.md"
+requires-python=">=3.9"
+keywords=[
+ "pytorch",
+ "machine-learning-interatomic-potentials",
+ "huggingface",
+ "deep-learning",
+ "graph-neural-networks",
+]
+classifiers=[
+ "Development Status :: 1 - Planning",
+ "Programming Language :: Python",
+ "Programming Language :: Python :: 3.10",
+ "Programming Language :: Python :: 3.11",
+ "Programming Language :: Python :: 3.12",
+ "Programming Language :: Python :: 3 :: Only",
+]
+dependencies=[
+ "loguru",
+ "ase",
+ "pymatgen",
+ "torch",
+ "huggingface_hub",
+ "datasets",
+ "safetensors",
+ "prefect>3.2.0",
+ "prefect-dask",
+ "dask",
+ "dask_jobqueue",
+ "tables",
+ "MDAnalysis", # mof
+]
+
+[project.optional-dependencies]
+app = [
+ "streamlit==1.43.2",
+ "plotly",
+]
+test = [
+ "torch==2.4.0",
+ "torch_dftd==0.4.0",
+ "e3nn==0.5.0",
+ "dgl",
+ "chgnet==0.3.8",
+ "sevenn==0.9.3.post1",
+ "alignn==2024.5.27",
+ "mattersim==1.1.2",
+ "torchani==2.2.4",
+ "pytest",
+ "pytest-xdist",
+ "prefect==3.2.13",
+ "pymatgen>=2025.1.9",
+ "MDAnalysis", # mof,
+ "streamlit==1.43.2"
+]
+mace = [
+ "mace-torch==0.3.12",
+]
+matgl = [
+ "matgl==1.2.6",
+]
+fairchem = [
+ "hydra-core",
+ "fairchem-core==1.10.0",
+]
+orb = [
+ "orb-models==0.4.0",
+ "pynanoflann@git+https://github.com/dwastberg/pynanoflann#egg=af434039ae14bedcbb838a7808924d6689274168",
+]
+deepmd = [
+ "torch==2.2.0",
+ "deepmd-kit@git+https://github.com/deepmodeling/deepmd-kit.git@v3.0.0b4"
+]
+
+[project.urls]
+Homepage = "https://github.com/atomind-ai/mlip-arena"
+Issues = "https://github.com/atomind-ai/mlip-arena/issues"
+
+[tool.ruff]
+# Exclude a variety of commonly ignored directories.
+extend-include = ["*.ipynb"]
+exclude = [
+ ".bzr",
+ ".direnv",
+ ".eggs",
+ ".git",
+ ".git-rewrite",
+ ".hg",
+ ".ipynb_checkpoints",
+ ".mypy_cache",
+ ".nox",
+ ".pants.d",
+ ".pyenv",
+ ".pytest_cache",
+ ".pytype",
+ ".ruff_cache",
+ ".svn",
+ ".tox",
+ ".venv",
+ ".vscode",
+ "__pypackages__",
+ "_build",
+ "buck-out",
+ "build",
+ "dist",
+ "node_modules",
+ "site-packages",
+ "venv",
+]
+
+# Same as Black.
+line-length = 88
+indent-width = 4
+
+[tool.ruff.lint]
+select = [
+ "B", # flake8-bugbear
+ "C4", # flake8-comprehensions
+ "E", # pycodestyle error
+ "EXE", # flake8-executable
+ "F", # pyflakes
+ "FA", # flake8-future-annotations
+ "FBT003", # boolean-positional-value-in-call
+ "FLY", # flynt
+ "I", # isort
+ "ICN", # flake8-import-conventions
+ "PD", # pandas-vet
+ "PERF", # perflint
+ "PIE", # flake8-pie
+ "PL", # pylint
+ "PT", # flake8-pytest-style
+ "PYI", # flakes8-pyi
+ "Q", # flake8-quotes
+ "RET", # flake8-return
+ "RSE", # flake8-raise
+ "RUF", # Ruff-specific rules
+ "SIM", # flake8-simplify
+ "SLOT", # flake8-slots
+ "TCH", # flake8-type-checking
+ "TID", # tidy imports
+ "TID", # flake8-tidy-imports
+ "UP", # pyupgrade
+ "W", # pycodestyle warning
+ "YTT", # flake8-2020
+]
+ignore = [
+ "C408", # Unnecessary dict call
+ "PLR", # Design related pylint codes
+ "E501", # Line too long
+ "B028", # No explicit stacklevel
+ "EM101", # Exception must not use a string literal
+ "EM102", # Exception must not use an f-string literal
+ "G004", # f-string in Logging statement
+ "RUF015", # Prefer next(iter())
+ "RET505", # Unnecessary `elif` after `return`
+ "PT004", # Fixture does not return anthing
+ "B017", # pytest.raises
+ "PT011", # pytest.raises
+ "PT012", # pytest.raises"
+ "E741", # ambigous variable naming, i.e. one letter
+ "FBT003", # boolean positional variable in function call
+ "PERF203", # `try`-`except` within a loop incurs performance overhead (no overhead in Py 3.11+)
+ "F405", # 'module' may be undefined, or defined from star imports
+ "PD901",
+]
+fixable = ["ALL"]
+pydocstyle.convention = "google"
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000000000000000000000000000000000000..27eec68ed708999a4b2ac00f8334a17f04c974cc
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,3 @@
+[pytest]
+testpaths = tests
+python_files = test_*.py
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b05420402e25f3a8588aa01a0981f0e5eb7ac496
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,14 @@
+streamlit>=1.43.0
+plotly
+numpy
+scipy
+ase==3.23.0
+torch==2.2.0
+pymatgen>=2025.1.9
+bokeh
+bokeh_sampledata
+statsmodels==0.14.2
+prefect==3.2.13
+loguru==0.7.3
+# py3Dmol==2.0.0.post2
+# stmol==0.0.9
diff --git a/scripts/install-dgl.sh b/scripts/install-dgl.sh
new file mode 100644
index 0000000000000000000000000000000000000000..96a7449d2fb61a4c15a1f6f6d14828ff2a0c255a
--- /dev/null
+++ b/scripts/install-dgl.sh
@@ -0,0 +1,6 @@
+# DGL (M3GNet, ALIGNN)
+
+TORCH=2.2
+CUDA=cu121
+
+pip install dgl -f https://data.dgl.ai/wheels/torch-${TORCH}/${CUDA}/repo.html
\ No newline at end of file
diff --git a/scripts/install-linux.sh b/scripts/install-linux.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c4fbd27e47c6d87cfd18bf5fd73e43fefcfa0981
--- /dev/null
+++ b/scripts/install-linux.sh
@@ -0,0 +1,10 @@
+TORCH=2.4
+CUDA=cu124
+uv pip install torch==${TORCH}.0
+uv pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.0+${CUDA}.html
+uv pip install dgl -f https://data.dgl.ai/wheels/torch-${TORCH}/${CUDA}/repo.html
+uv pip install -e .[fairchem]
+uv pip install -e .[orb]
+uv pip install -e .[matgl]
+uv pip install -e .[test]
+uv pip install -e .[mace]
\ No newline at end of file
diff --git a/scripts/install-macosx.sh b/scripts/install-macosx.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1c1b6c90741d13f46a2365c3394e819974277476
--- /dev/null
+++ b/scripts/install-macosx.sh
@@ -0,0 +1,21 @@
+
+
+# (Optional) Install uv
+# curl -LsSf https://astral.sh/uv/install.sh | sh
+# source $HOME/.local/bin/env
+
+TORCH=2.2.0
+
+uv pip install torch==${TORCH}
+uv pip install torch-scatter --no-build-isolation
+uv pip install torch-sparse --no-build-isolation
+
+uv pip install dgl -f https://data.dgl.ai/wheels/torch-${TORCH}/cpu/repo.html
+
+uv pip install mlip-arena[fairchem]
+uv pip install mlip-arena[orb]
+uv pip install mlip-arena[matgl]
+uv pip install mlip-arena[test]
+uv pip install mlip-arena[mace]
+
+
diff --git a/scripts/install-pyg.sh b/scripts/install-pyg.sh
new file mode 100755
index 0000000000000000000000000000000000000000..49b0ba1d6feee6e3d7a44a480ccb51a30ad6d594
--- /dev/null
+++ b/scripts/install-pyg.sh
@@ -0,0 +1,6 @@
+# PyTorch Geometric (OCP)
+TORCH=2.2.0
+CUDA=cu121
+
+pip install --verbose --no-cache torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html
+pip install --verbose --no-cache torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html
diff --git a/scripts/install.sh b/scripts/install.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1ced2ff8dd2184e2b44d58afcd39f16df88e696a
--- /dev/null
+++ b/scripts/install.sh
@@ -0,0 +1,10 @@
+TORCH=2.4
+CUDA=cu124
+uv pip install torch==${TORCH}.0
+uv pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.0+${CUDA}.html
+uv pip install dgl -f https://data.dgl.ai/wheels/torch-${TORCH}/${CUDA}/repo.html
+uv pip install mlip-arena[fairchem]
+uv pip install mlip-arena[orb]
+uv pip install mlip-arena[matgl]
+uv pip install mlip-arena[test]
+uv pip install mlip-arena[mace]
\ No newline at end of file
diff --git a/serve/README.md b/serve/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/serve/app.py b/serve/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5d15c6cd209b25a543d607401da522c454ed2c6
--- /dev/null
+++ b/serve/app.py
@@ -0,0 +1,70 @@
+from collections import defaultdict
+
+import streamlit as st
+
+from mlip_arena.tasks import REGISTRY as TASKS
+
+
+leaderboard = st.Page(
+ "leaderboard.py", title="Leaderboard", icon=":material/trophy:", default=True
+)
+
+nav = defaultdict(list)
+nav[""].append(leaderboard)
+
+wide_pages, centered_pages = [], []
+
+for task in TASKS:
+ if TASKS[task]['task-page'] is None:
+ continue
+ page = st.Page(
+ f"tasks/{TASKS[task]['task-page']}.py", title=task, icon=":material/target:"
+ )
+ nav[TASKS[task]["category"]].append(page)
+ if TASKS[task]["task-layout"] == "wide":
+ wide_pages.append(page)
+ else:
+ centered_pages.append(page)
+
+pg = st.navigation(nav, expanded=True)
+
+if pg in centered_pages:
+ st.set_page_config(
+ layout="centered",
+ page_title="MLIP Arena",
+ page_icon=":shark:",
+ initial_sidebar_state="expanded",
+ menu_items={
+ "About": "https://github.com/atomind-ai/mlip-arena",
+ "Report a bug": "https://github.com/atomind-ai/mlip-arena/issues/new",
+ },
+ )
+else:
+ st.set_page_config(
+ layout="wide",
+ page_title="MLIP Arena",
+ page_icon=":shark:",
+ initial_sidebar_state="expanded",
+ menu_items={
+ "About": "https://github.com/atomind-ai/mlip-arena",
+ "Report a bug": "https://github.com/atomind-ai/mlip-arena/issues/new",
+ },
+ )
+
+st.sidebar.page_link(
+ "https://github.com/atomind-ai/mlip-arena", label="GitHub Repository", icon=":material/code:"
+)
+
+st.sidebar.markdown(
+"""
+Complementary Benchmarks
+"""
+)
+st.sidebar.page_link(
+ "https://matbench-discovery.materialsproject.org/", label="Matbench Discovery", icon=":material/link:"
+)
+st.sidebar.page_link(
+ "https://openkim.org/", label="OpenKIM", icon=":material/link:"
+)
+
+pg.run()
diff --git a/serve/assets/workflow.png b/serve/assets/workflow.png
new file mode 100644
index 0000000000000000000000000000000000000000..16f7a81c5281ebee8d810223dcb7cec920de8a9f
--- /dev/null
+++ b/serve/assets/workflow.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:292d62a95c5d14b39dbe38200ca9f2ee80b67debae011beb9398d6c4fcc101af
+size 320588
diff --git a/serve/leaderboard.py b/serve/leaderboard.py
new file mode 100644
index 0000000000000000000000000000000000000000..57b38948c2399e18ba0ef0197e50a179dca35472
--- /dev/null
+++ b/serve/leaderboard.py
@@ -0,0 +1,146 @@
+import importlib
+from pathlib import Path
+
+import pandas as pd
+import streamlit as st
+
+from mlip_arena import PKG_DIR
+from mlip_arena.models import REGISTRY as MODELS
+from mlip_arena.tasks import REGISTRY as TASKS
+
+# Read the data
+DATA_DIR = PKG_DIR / "tasks" /"diatomics"
+
+dfs = []
+for model in MODELS:
+ fpath = DATA_DIR / MODELS[model].get("family") / "homonuclear-diatomics.json"
+ if fpath.exists():
+ dfs.append(pd.read_json(fpath))
+df = pd.concat(dfs, ignore_index=True)
+
+# Create a table
+table = pd.DataFrame(
+ columns=[
+ "Model",
+ "Element Coverage",
+ "Prediction",
+ "NVT",
+ "NPT",
+ "Training Set",
+ "Code",
+ "Paper",
+ "Checkpoint",
+ "First Release",
+ "License",
+ ]
+)
+
+for model in MODELS:
+ rows = df[df["method"] == model]
+ metadata = MODELS.get(model, {})
+ new_row = {
+ "Model": model,
+ "Element Coverage": len(rows["name"].unique()),
+ "Prediction": metadata.get("prediction", None),
+ "NVT": "โ " if metadata.get("nvt", False) else "โ",
+ "NPT": "โ " if metadata.get("npt", False) else "โ",
+ "Training Set": metadata.get("datasets", []),
+ "Code": metadata.get("github", None) if metadata else None,
+ "Paper": metadata.get("doi", None) if metadata else None,
+ "Checkpoint": metadata.get("checkpoint", None),
+ "First Release": metadata.get("date", None),
+ "License": metadata.get("license", None),
+ }
+ table = pd.concat([table, pd.DataFrame([new_row])], ignore_index=True)
+
+table.set_index("Model", inplace=True)
+
+s = table.style.background_gradient(
+ cmap="PuRd", subset=["Element Coverage"], vmin=0, vmax=120
+)
+
+# st.warning(
+# "MLIP Arena is currently in **pre-alpha**. The results are not stable. Please interpret them with care.",
+# icon="โ ๏ธ",
+# )
+st.info(
+ "Contributions are welcome. For more information, visit https://github.com/atomind-ai/mlip-arena.",
+ icon="๐ค",
+)
+
+st.markdown(
+ """
+
โ๏ธ MLIP Arena Leaderboard โ๏ธ
+
+
+
+
+
+
+
+
+
+
+
+
+> MLIP Arena is a unified platform for evaluating foundation machine learning interatomic potentials (MLIPs) beyond conventional energy and force error metrics. It focuses on revealing the underlying physics and chemistry learned by these models. The platform's benchmarks are specifically designed to evaluate the readiness and reliability of open-source, open-weight models in accurately reproducing both qualitative and quantitative behaviors of atomic systems.
+
+### :red[Introduction]
+
+Foundation machine learning interatomic potentials (MLIPs), trained on extensive databases containing millions of density functional theory (DFT) calculations,have revolutionized molecular and materials modeling, but existing benchmarks suffer from data leakage, limited transferability, and an over-reliance on error-based metrics tied to specific density functional theory (DFT) references.
+
+We introduce MLIP Arena, a unified benchmark platform for evaluating foundation MLIP performance beyond conventional error metrics. It focuses on revealing the physical soundness learned by MLIPs and assessing their utilitarian performance agnostic to underlying model architecture and training dataset.
+
+***By moving beyond static DFT references and revealing the important failure modes*** of current foundation MLIPs in real-world settings, MLIP Arena provides a reproducible framework to guide the next-generation MLIP development toward improved predictive accuracy and runtime efficiency while maintaining physical consistency.
+
+""",
+ unsafe_allow_html=True,
+)
+
+
+st.subheader(":red[Supported Models]")
+st.dataframe(
+ s,
+ use_container_width=True,
+ column_config={
+ "Code": st.column_config.LinkColumn(
+ # validate="^https://[a-z]+\.streamlit\.app$",
+ width="medium",
+ display_text="Link",
+ ),
+ "Paper": st.column_config.LinkColumn(
+ # validate="^https://[a-z]+\.streamlit\.app$",
+ width="medium",
+ display_text="Link",
+ ),
+ },
+)
+
+# st.markdown("
๐ Task Ranks ๐
", unsafe_allow_html=True)
+
+st.subheader(":red[Task Ranks]")
+
+for task in TASKS:
+ if TASKS[task]["rank-page"] is None:
+ continue
+
+ st.subheader(task, divider=True)
+
+ task_module = importlib.import_module(f"ranks.{TASKS[task]['rank-page']}")
+
+ if TASKS[task]['task-page'] is not None:
+ st.page_link(
+ f"tasks/{TASKS[task]['task-page']}.py",
+ label="Go to the associated task page",
+ icon=":material/link:",
+ )
+
+ # Call the function from the imported module
+ if hasattr(task_module, "render"):
+ task_module.render()
+ # if st.button(f"Go to task page"):
+ # st.switch_page(f"tasks/{TASKS[task]['task-page']}.py")
+ else:
+ st.write(
+ "Rank metrics are not available yet but the task has been implemented. Please see the task page for more information."
+ )
diff --git a/serve/ranks/combustion.py b/serve/ranks/combustion.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc42e659f85284c30b7edc689fd3c1239fa653b1
--- /dev/null
+++ b/serve/ranks/combustion.py
@@ -0,0 +1,150 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+from mlip_arena.models import REGISTRY as MODELS
+
+valid_models = [
+ model
+ for model, metadata in MODELS.items()
+ if Path(__file__).stem in metadata.get("gpu-tasks", [])
+]
+
+DATA_DIR = Path("mlip_arena/tasks/combustion")
+
+@st.cache_data
+def get_data(models):
+ families = [MODELS[str(model)]["family"] for model in models]
+ dfs = [
+ pd.read_json(DATA_DIR / family.lower() / "hydrogen.json") for family in families
+ ]
+ df = pd.concat(dfs, ignore_index=True)
+ df.drop_duplicates(inplace=True, subset=["formula", "method"])
+ return df
+
+df = get_data(valid_models)
+
+@st.cache_data
+def get_com_drifts(df):
+ df_exploded = df.explode(["timestep", "energies", "com_drifts"]).reset_index(drop=True)
+
+ # Convert the 'com_drifts' column (which are arrays) into separate columns for x, y, and z components
+ df_exploded[["com_drift_x", "com_drift_y", "com_drift_z"]] = pd.DataFrame(
+ df_exploded["com_drifts"].tolist(), index=df_exploded.index
+ )
+
+ # Drop the original 'com_drifts' column
+ df_flat = df_exploded.drop(columns=["com_drifts"])
+
+ df_flat["total_com_drift"] = np.sqrt(
+ df_flat["com_drift_x"] ** 2 + df_flat["com_drift_y"] ** 2 + df_flat["com_drift_z"] ** 2
+ )
+
+ df_flat = df_flat.drop(columns=["com_drift_x", "com_drift_y", "com_drift_z"])
+
+ return df_flat
+
+df_exploded = get_com_drifts(df)
+
+exp_ref = -68.3078 # kcal/mol
+
+for method, row in df_exploded.groupby("method"):
+# # row = df[df["method"] == method].iloc[0]
+ energies = np.array(row["energies"])
+ df_exploded.loc[df_exploded["method"] == method,"reaction_enthlapy_diff"] = ((energies[-1] - energies[0]) / 128 * 23.) - exp_ref
+ df_exploded.loc[df_exploded["method"] == method, "final_com_drift"] = np.array(row["total_com_drift"])[-1]
+
+
+df_exploded.drop(columns=["temperatures", "pressures", "total_steps", "energies", "kinetic_energies", "timestep", "nproducts", "total_com_drift", "target_steps", "reaction", "formula", "natoms", "seconds_per_step", "seconds_per_step_per_atom", "final_step", "total_time_seconds"], axis=1, inplace=True)
+
+df_exploded.drop_duplicates(inplace=True, subset=["method"])
+
+print(df_exploded.columns)
+
+df_exploded.set_index("method", inplace=True)
+
+df_exploded.rename(columns={
+ "method": "Model"
+}, inplace=True)
+
+
+table = pd.DataFrame()
+
+for index, row in df_exploded.iterrows():
+
+ new_row = {
+ "Model": index,
+ "Reaction enthalpy error [kcal/mol]": row["reaction_enthlapy_diff"],
+ "Final COM drift [โซ]": row["final_com_drift"],
+ "Steps per second": row["steps_per_second"],
+ "Yield [%]": row["yield"] * 100,
+ }
+
+ table = pd.concat([table, pd.DataFrame([new_row])], ignore_index=True)
+
+table.set_index("Model", inplace=True)
+
+table.sort_values("Reaction enthalpy error [kcal/mol]", ascending=True, inplace=True)
+table["Rank"] = np.argsort(np.abs(table["Reaction enthalpy error [kcal/mol]"].to_numpy()))
+
+table.sort_values("Final COM drift [โซ]", ascending=True, inplace=True)
+table["Rank"] += np.argsort(table["Final COM drift [โซ]"].to_numpy())
+
+table.sort_values("Steps per second", ascending=False, inplace=True)
+table["Rank"] += np.argsort(-table["Steps per second"].to_numpy())
+
+table.sort_values("Yield [%]", ascending=False, inplace=True)
+table["Rank"] += np.argsort(-table["Yield [%]"].to_numpy())
+
+table["Rank"] += 1
+
+table.sort_values(["Rank"], ascending=True, inplace=True)
+
+table["Rank aggr."] = table["Rank"]
+table["Rank"] = table["Rank aggr."].rank(method='min').astype(int)
+
+
+table = table.reindex(
+ columns=[
+ "Rank",
+ "Rank aggr.",
+ "Reaction enthalpy error [kcal/mol]",
+ "Final COM drift [โซ]",
+ "Steps per second",
+ "Yield [%]",
+ ]
+)
+
+s = (
+ table.style.background_gradient(
+ cmap="Oranges",
+ subset=["Reaction enthalpy error [kcal/mol]"],
+ )
+ .background_gradient(
+ cmap="Oranges",
+ subset=["Final COM drift [โซ]"],
+ gmap=np.log10(table["Final COM drift [โซ]"].to_numpy() + 1e-10),
+ )
+ .background_gradient(
+ cmap="Oranges_r",
+ subset=["Steps per second", "Yield [%]"]
+ )
+ .background_gradient(
+ cmap="Blues",
+ subset=["Rank", "Rank aggr."],
+ )
+ .format(
+ "{:.3e}",
+ subset=["Final COM drift [โซ]"],
+ )
+)
+
+
+def render():
+
+ st.dataframe(
+ s,
+ use_container_width=True,
+ )
diff --git a/serve/ranks/eos_bulk.py b/serve/ranks/eos_bulk.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2ed1d9f1574afd4a9c8d8e07ee2d6bd51e21680
--- /dev/null
+++ b/serve/ranks/eos_bulk.py
@@ -0,0 +1,63 @@
+from pathlib import Path
+
+import pandas as pd
+import streamlit as st
+
+DATA_DIR = Path("benchmarks/eos_bulk")
+
+
+table = pd.read_csv(DATA_DIR / "summary.csv")
+
+
+
+table = table.rename(
+ columns={
+ "model": "Model",
+ "rank": "Rank",
+ "rank-aggregation": "Rank aggr.",
+ "energy-diff-flip-times": "Derivative flips",
+ "tortuosity": "Tortuosity",
+ "spearman-compression-energy": "Spearman's coeff. (compression)",
+ "spearman-tension-energy": "Spearman's coeff. (tension)",
+ "spearman-compression-derivative": "Spearman's coeff. (compression derivative)",
+ "missing": "Missing",
+ },
+)
+
+table.set_index("Model", inplace=True)
+
+s = (
+ table.style.background_gradient(
+ cmap="Blues",
+ subset=["Rank", "Rank aggr."],
+ ).background_gradient(
+ cmap="Reds",
+ subset=[
+ "Spearman's coeff. (compression)",
+ ],
+ ).background_gradient(
+ cmap="Reds_r",
+ subset=[
+ "Spearman's coeff. (tension)",
+ "Spearman's coeff. (compression derivative)",
+ ],
+ ).background_gradient(
+ cmap="RdPu",
+ subset=["Tortuosity", "Derivative flips"],
+ ).format(
+ "{:.5f}",
+ subset=[
+ "Spearman's coeff. (compression)",
+ "Spearman's coeff. (tension)",
+ "Spearman's coeff. (compression derivative)",
+ "Tortuosity",
+ "Derivative flips",
+ ],
+ )
+)
+
+def render():
+ st.dataframe(
+ s,
+ use_container_width=True,
+ )
diff --git a/serve/ranks/homonuclear-diatomics.py b/serve/ranks/homonuclear-diatomics.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5d635e2b37b45eef6d5da9a93ded1c01e23129c
--- /dev/null
+++ b/serve/ranks/homonuclear-diatomics.py
@@ -0,0 +1,206 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+from mlip_arena.models import REGISTRY as MODELS
+
+valid_models = [
+ model
+ for model, metadata in MODELS.items()
+ if Path(__file__).stem in metadata.get("gpu-tasks", [])
+]
+
+DATA_DIR = Path("mlip_arena/tasks/diatomics")
+
+dfs = [
+ pd.read_json(DATA_DIR / MODELS[model].get("family") / "homonuclear-diatomics.json")
+ for model in valid_models
+]
+df = pd.concat(dfs, ignore_index=True)
+
+# df = df[df["method"].isin([
+# "SevenNet",
+# "ORBv2",
+# "ORB",
+# "MatterSim",
+# "MACE-MPA",
+# "MACE-MP(M)",
+# "M3GNet",
+# "eSEN",
+# "eSCN(OC20)",
+# "eqV2(OMat)",
+# "EquiformerV2(OC22)",
+# "EquiformerV2(OC20)",
+# "CHGNet",
+# "ALIGNN"
+# ]
+# )]
+
+table = pd.DataFrame()
+
+for model in valid_models:
+ rows = df[df["method"] == model]
+ metadata = MODELS.get(model, {})
+
+ new_row = {
+ "Model": model,
+ "Conservation deviation [eV/โซ]": rows["conservation-deviation"].mean(),
+ "Spearman's coeff. (E: repulsion)": rows["spearman-repulsion-energy"].mean(),
+ "Spearman's coeff. (F: descending)": rows["spearman-descending-force"].mean(),
+ "Tortuosity": rows["tortuosity"].mean(),
+ "Energy jump [eV]": rows["energy-jump"].mean(),
+ "Force flips": rows["force-flip-times"].mean(),
+ "Spearman's coeff. (E: attraction)": rows["spearman-attraction-energy"].mean(),
+ "Spearman's coeff. (F: ascending)": rows["spearman-ascending-force"].mean(),
+ "PBE energy MAE [eV]": rows["pbe-energy-mae"].mean(),
+ "PBE force MAE [eV/โซ]": rows["pbe-force-mae"].mean(),
+ }
+
+ table = pd.concat([table, pd.DataFrame([new_row])], ignore_index=True)
+
+table.set_index("Model", inplace=True)
+
+table.sort_values("Conservation deviation [eV/โซ]", ascending=True, inplace=True)
+table["Rank"] = np.argsort(table["Conservation deviation [eV/โซ]"].to_numpy())
+
+table.sort_values("Spearman's coeff. (E: repulsion)", ascending=True, inplace=True)
+table["Rank"] += np.argsort(table["Spearman's coeff. (E: repulsion)"].to_numpy())
+
+table.sort_values("Spearman's coeff. (F: descending)", ascending=True, inplace=True)
+table["Rank"] += np.argsort(table["Spearman's coeff. (F: descending)"].to_numpy())
+
+# NOTE: it's not fair to models trained on different level of theory
+# table.sort_values("PBE energy MAE [eV]", ascending=True, inplace=True)
+# table["Rank"] += np.argsort(table["PBE energy MAE [eV]"].to_numpy())
+
+# table.sort_values("PBE force MAE [eV/โซ]", ascending=True, inplace=True)
+# table["Rank"] += np.argsort(table["PBE force MAE [eV/โซ]"].to_numpy())
+
+table.sort_values("Tortuosity", ascending=True, inplace=True)
+table["Rank"] += np.argsort(table["Tortuosity"].to_numpy())
+
+table.sort_values("Energy jump [eV]", ascending=True, inplace=True)
+table["Rank"] += np.argsort(table["Energy jump [eV]"].to_numpy())
+
+table.sort_values("Force flips", ascending=True, inplace=True)
+table["Rank"] += np.argsort(np.abs(table["Force flips"].to_numpy() - 1))
+
+table["Rank"] += 1
+
+table.sort_values(
+ ["Rank", "Conservation deviation [eV/โซ]"], ascending=True, inplace=True
+)
+
+table["Rank aggr."] = table["Rank"]
+table["Rank"] = table["Rank aggr."].rank(method="min").astype(int)
+
+table = table.reindex(
+ columns=[
+ "Rank",
+ "Rank aggr.",
+ "Conservation deviation [eV/โซ]",
+ "Spearman's coeff. (E: repulsion)",
+ "Spearman's coeff. (F: descending)",
+ "Energy jump [eV]",
+ "Force flips",
+ "Tortuosity",
+ "PBE energy MAE [eV]",
+ "PBE force MAE [eV/โซ]",
+ "Spearman's coeff. (E: attraction)",
+ "Spearman's coeff. (F: ascending)",
+ ]
+)
+
+# cloned = table.copy()
+# cloned.drop(columns=[
+# "PBE energy MAE [eV]",
+# "PBE force MAE [eV/โซ]",
+# "Spearman's coeff. (E: attraction)",
+# "Spearman's coeff. (F: ascending)",],
+# inplace=True
+# )
+# cloned.to_latex(
+# DATA_DIR / "homonuclear-diatomics.tex",
+# float_format="%.3f",
+# index=True,
+# column_format="l" + "r" * (len(table.columns) - 1),
+# )
+
+s = (
+ table.style.background_gradient(
+ cmap="viridis_r",
+ subset=["Conservation deviation [eV/โซ]"],
+ gmap=np.log(table["Conservation deviation [eV/โซ]"].to_numpy()),
+ )
+ .background_gradient(
+ cmap="Reds",
+ subset=[
+ "Spearman's coeff. (E: repulsion)",
+ "Spearman's coeff. (F: descending)",
+ ],
+ # vmin=-1, vmax=-0.5
+ )
+ # .background_gradient(
+ # cmap="Greys",
+ # subset=[
+ # "PBE energy MAE [eV]",
+ # "PBE force MAE [eV/โซ]",
+ # ],
+ # )
+ .background_gradient(
+ cmap="RdPu",
+ subset=["Tortuosity", "Energy jump [eV]", "Force flips"],
+ )
+ .background_gradient(
+ cmap="Blues",
+ subset=["Rank", "Rank aggr."],
+ )
+ .format(
+ "{:.3f}",
+ subset=[
+ "Conservation deviation [eV/โซ]",
+ "Spearman's coeff. (E: repulsion)",
+ "Spearman's coeff. (F: descending)",
+ "Tortuosity",
+ "Energy jump [eV]",
+ "Force flips",
+ "Spearman's coeff. (E: attraction)",
+ "Spearman's coeff. (F: ascending)",
+ "PBE energy MAE [eV]",
+ "PBE force MAE [eV/โซ]",
+ ],
+ )
+)
+
+
+def render():
+ st.dataframe(
+ s,
+ use_container_width=True,
+ )
+ with st.expander("Explanation", icon=":material/info:"):
+ st.caption(
+ r"""
+ - **Conservation deviation**: The average deviation of force from negative energy gradient along the diatomic curves.
+
+ $$
+ \text{Conservation deviation} = \left\langle\left| \mathbf{F}(\mathbf{r})\cdot\frac{\mathbf{r}}{\|\mathbf{r}\|} + \nabla_rE\right|\right\rangle_{r = \|\mathbf{r}\|}
+ $$
+
+ - **Spearman's coeff. (E: repulsion)**: Spearman's correlation coefficient of energy prediction within equilibrium distance $r \in (r_{min}, r_o = \argmin_{r} E(r))$.
+ - **Spearman's coeff. (F: descending)**: Spearman's correlation coefficient of force prediction before maximum attraction $r \in (r_{min}, r_a = \argmin_{r} F(r))$.
+ - **Tortuosity**: The ratio between total variation in energy and sum of absolute energy differences between $r_{min}$, $r_o$, and $r_{max}$.
+ - **Energy jump**: The sum of energy discontinuity between sampled points.
+
+ $$
+ \text{Energy jump} = \sum_{r_i \in [r_\text{min}, r_\text{max}]} \left| \text{sign}{\left[ E(r_{i+1}) - E(r_i)\right]} - \text{sign}{\left[E(r_i) - E(r_{i-1})\right]}\right| \times \\ \left( \left|E(r_{i+1}) - E(r_i)\right| + \left|E(r_i) - E(r_{i-1})\right|\right)
+ $$
+ - **Force flips**: The number of force direction changes.
+ """
+ )
+ st.info(
+ "PBE energies and forces are provided __only__ for reference. Due to the known convergence issue of plane-wave DFT with diatomic molecules and different dataset the models might be trained on, comparing models with PBE is not rigorous and thus these metrics are excluded from rank aggregation.",
+ icon=":material/warning:",
+ )
diff --git a/serve/ranks/thermal-conductivity.py b/serve/ranks/thermal-conductivity.py
new file mode 100644
index 0000000000000000000000000000000000000000..38815e09f22673ededbbbe6c5298642328b80edd
--- /dev/null
+++ b/serve/ranks/thermal-conductivity.py
@@ -0,0 +1,60 @@
+
+import pandas as pd
+import streamlit as st
+
+from mlip_arena import PKG_DIR
+
+DATA_DIR = PKG_DIR / "tasks" / "thermal-conductivity"
+
+table = pd.read_csv(DATA_DIR / "wte.csv")
+
+table.rename(
+ columns={
+ "method": "Model",
+ "srme": "SRME[๐ ]",
+ },
+ inplace=True,
+)
+
+table.set_index("Model", inplace=True)
+
+table.sort_values(["SRME[๐ ]"], ascending=True, inplace=True)
+
+table["Rank"] = table["SRME[๐ ]"].rank(method='min').astype(int)
+
+table = table.reindex(
+ columns=[
+ "Rank",
+ "SRME[๐ ]",
+ ]
+)
+
+s = (
+ table.style.background_gradient(
+ cmap="Reds", subset=["SRME[๐ ]"]
+ )
+ .background_gradient(
+ cmap="Blues",
+ subset=["Rank"],
+ )
+ .format("{:.3f}", subset=["SRME[๐ ]"])
+)
+
+
+def render():
+
+ st.dataframe(
+ s,
+ use_container_width=True
+ )
+
+ with st.expander("Explanation", icon=":material/info:"):
+ st.caption(
+ """
+ - **SRME**: symmetric relative mean error of single-phonon conductivity:
+
+ $$
+ \\text{SRME}[\\left\lbrace\\mathcal{K}({\\mathbf{q},s)}\\right\\rbrace] = \\frac{2}{N_qV}\\frac{\\sum_{\\mathbf{q}s}|\\mathcal{K}_{\\text{MLIP}}(\\mathbf{q},s) - \\mathcal{K}_{\\text{DFT}}(\\mathbf{q},s)|}{\\kappa_{\\text{MLIP}} + \\kappa_{\\text{DFT}}}
+ $$
+ """
+ )
diff --git a/serve/ranks/wbm_ev.py b/serve/ranks/wbm_ev.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e3a29606dd801cfe64ae096df5d285957ef0372
--- /dev/null
+++ b/serve/ranks/wbm_ev.py
@@ -0,0 +1,63 @@
+from pathlib import Path
+
+import pandas as pd
+import streamlit as st
+
+DATA_DIR = Path("benchmarks/wbm_ev")
+
+
+table = pd.read_csv(DATA_DIR / "summary.csv")
+
+
+
+table = table.rename(
+ columns={
+ "model": "Model",
+ "rank": "Rank",
+ "rank-aggregation": "Rank aggr.",
+ "energy-diff-flip-times": "Derivative flips",
+ "tortuosity": "Tortuosity",
+ "spearman-compression-energy": "Spearman's coeff. (compression)",
+ "spearman-tension-energy": "Spearman's coeff. (tension)",
+ "spearman-compression-derivative": "Spearman's coeff. (compression derivative)",
+ "missing": "Missing",
+ },
+)
+
+table.set_index("Model", inplace=True)
+
+s = (
+ table.style.background_gradient(
+ cmap="Blues",
+ subset=["Rank", "Rank aggr."],
+ ).background_gradient(
+ cmap="Reds",
+ subset=[
+ "Spearman's coeff. (compression)",
+ ],
+ ).background_gradient(
+ cmap="Reds_r",
+ subset=[
+ "Spearman's coeff. (tension)",
+ "Spearman's coeff. (compression derivative)",
+ ],
+ ).background_gradient(
+ cmap="RdPu",
+ subset=["Tortuosity", "Derivative flips"],
+ ).format(
+ "{:.5f}",
+ subset=[
+ "Spearman's coeff. (compression)",
+ "Spearman's coeff. (tension)",
+ "Spearman's coeff. (compression derivative)",
+ "Tortuosity",
+ "Derivative flips",
+ ],
+ )
+)
+
+def render():
+ st.dataframe(
+ s,
+ use_container_width=True,
+ )
diff --git a/serve/tasks/combustion.py b/serve/tasks/combustion.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fcbbb58097b62b16e8fd9b201de7b04285a6e17
--- /dev/null
+++ b/serve/tasks/combustion.py
@@ -0,0 +1,540 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import plotly.colors as pcolors
+import plotly.express as px
+import plotly.graph_objects as go
+import streamlit as st
+from mlip_arena.models import REGISTRY as MODELS
+
+DATA_DIR = Path("mlip_arena/tasks/combustion")
+
+
+st.markdown("""
+# Combustion
+""")
+
+st.markdown("### Methods")
+container = st.container(border=True)
+valid_models = [
+ model
+ for model, metadata in MODELS.items()
+ if Path(__file__).stem in metadata.get("gpu-tasks", [])
+]
+
+models = container.multiselect(
+ "MLIPs",
+ valid_models,
+ [
+ "MACE-MP(M)",
+ "CHGNet",
+ "M3GNet",
+ "SevenNet",
+ "ORB",
+ "ORBv2",
+ "EquiformerV2(OC20)",
+ "eSCN(OC20)",
+ "MatterSim",
+ ],
+)
+
+st.markdown("### Settings")
+vis = st.container(border=True)
+# Get all attributes from pcolors.qualitative
+all_attributes = dir(pcolors.qualitative)
+color_palettes = {
+ attr: getattr(pcolors.qualitative, attr)
+ for attr in all_attributes
+ if isinstance(getattr(pcolors.qualitative, attr), list)
+}
+color_palettes.pop("__all__", None)
+
+palette_names = list(color_palettes.keys())
+palette_colors = list(color_palettes.values())
+palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
+
+color_sequence = color_palettes[palette_name]
+
+if not models:
+ st.stop()
+
+
+@st.cache_data
+def get_data(models):
+ # List comprehension for concise looping and filtering
+ dfs = [
+ pd.read_json(DATA_DIR / MODELS[str(model)]["family"].lower() / "hydrogen.json")[
+ lambda df: df["method"] == model
+ ]
+ for model in models
+ ]
+ # Concatenate all filtered DataFrames
+ return pd.concat(dfs, ignore_index=True)
+
+
+df = get_data(models)
+
+method_color_mapping = {
+ method: color_sequence[i % len(color_sequence)]
+ for i, method in enumerate(df["method"].unique())
+}
+
+###
+
+# Number of products
+fig = go.Figure()
+
+for method in df["method"].unique():
+ row = df[df["method"] == method].iloc[0]
+ fig.add_trace(
+ go.Scatter(
+ x=row["timestep"],
+ y=row["nproducts"],
+ mode="lines",
+ name=method,
+ line=dict(color=method_color_mapping[method]),
+ showlegend=True,
+ ),
+ )
+
+fig.add_vrect(
+ x0=512345.94,
+ x1=666667,
+ fillcolor="lightblue",
+ opacity=0.2,
+ layer="below",
+ line_width=0,
+ annotation_text="Flame Temp. [1]",
+ annotation_position="top",
+)
+
+fig.update_layout(
+ title="Hydrogen Combustion (2H2 + O2 -> 2H2O, 64 units)",
+ xaxis_title="Timestep",
+ yaxis_title="Number of water molecules",
+)
+
+st.plotly_chart(fig)
+
+# tempearture
+
+fig = go.Figure()
+
+for method in df["method"].unique():
+ row = df[df["method"] == method].iloc[0]
+ fig.add_trace(
+ go.Scatter(
+ x=row["timestep"],
+ y=row["temperatures"],
+ mode="markers",
+ name=method,
+ line=dict(
+ color=method_color_mapping[method],
+ # width=1
+ ),
+ marker=dict(color=method_color_mapping[method], size=3),
+ showlegend=True,
+ ),
+ )
+
+target_steps = df["target_steps"].iloc[0]
+fig.add_trace(
+ go.Line(
+ x=[0, target_steps / 3, target_steps / 3 * 2, target_steps],
+ y=[300, 3000, 3000, 300],
+ mode="lines",
+ name="Target",
+ line=dict(dash="dash", color="white"),
+ showlegend=True,
+ ),
+)
+
+fig.add_vrect(
+ x0=512345.94,
+ x1=666667,
+ fillcolor="lightblue",
+ opacity=0.2,
+ layer="below",
+ line_width=0,
+ annotation_text="Flame Temp.",
+ annotation_position="top",
+)
+
+fig.update_layout(
+ # title="Hydrogen Combustion (2H2 + O2 -> 2H2O, 64 units)",
+ xaxis_title="Timestep",
+ yaxis_title="Temperature (K)",
+ # yaxis2=dict(
+ # title="Product Percentage (%)",
+ # overlaying="y",
+ # side="right",
+ # range=[0, 100],
+ # tickmode="sync",
+ # ),
+ # template="plotly_dark",
+)
+
+st.plotly_chart(fig)
+
+# Energy
+
+exp_ref = -68.3078 # kcal/mol
+factor = 23.0609
+nh2os = 128
+
+fig = go.Figure()
+
+for method in df["method"].unique():
+ row = df[df["method"] == method].iloc[0]
+ fig.add_trace(
+ go.Scatter(
+ x=row["timestep"],
+ y=(np.array(row["energies"]) - row["energies"][0]) / nh2os * factor,
+ mode="lines",
+ name=method,
+ line=dict(
+ color=method_color_mapping[method],
+ # width=1
+ ),
+ marker=dict(color=method_color_mapping[method], size=3),
+ showlegend=True,
+ ),
+ )
+
+target_steps = df["target_steps"].iloc[0]
+
+fig.add_shape(
+ go.layout.Shape(
+ type="line",
+ x0=0,
+ x1=target_steps,
+ y0=exp_ref,
+ y1=exp_ref, # y-values for the horizontal line
+ line=dict(color="Red", width=2, dash="dash"),
+ layer="below",
+ )
+)
+
+fig.add_annotation(
+ go.layout.Annotation(
+ x=0.5,
+ xref="paper",
+ xanchor="center",
+ y=exp_ref,
+ yanchor="bottom",
+ text=f"Experiment: {exp_ref} kcal/mol [2]",
+ showarrow=False,
+ font=dict(
+ color="Red",
+ ),
+ )
+)
+
+fig.add_vrect(
+ x0=512345.94,
+ x1=666667,
+ fillcolor="lightblue",
+ opacity=0.2,
+ layer="below",
+ line_width=0,
+ annotation_text="Flame Temp.",
+ annotation_position="top",
+)
+
+
+fig.update_layout(
+ xaxis_title="Timestep [2] Lide, D. R. (Ed.). (2004). CRC handbook of chemistry and physics (Vol. 85). CRC press.",
+ yaxis_title="๐ซE (kcal/mol)",
+)
+
+st.plotly_chart(fig)
+
+# Reaction energy
+
+fig = go.Figure()
+
+
+df["reaction_energy"] = (
+ df["energies"].apply(lambda x: x[-1] - x[0]) / nh2os * factor
+) # kcal/mol
+
+df["reaction_energy_abs_err"] = np.abs(df["reaction_energy"] - exp_ref)
+
+df.sort_values("reaction_energy_abs_err", inplace=True)
+
+fig.add_traces(
+ [
+ go.Bar(
+ x=df["method"],
+ y=df["reaction_energy"],
+ marker=dict(
+ color=[method_color_mapping[method] for method in df["method"]]
+ ),
+ text=[f"{y:.2f}" for y in df["reaction_energy"]],
+ ),
+ ]
+)
+
+fig.add_shape(
+ go.layout.Shape(
+ type="line",
+ x0=-0.5,
+ x1=len(df["method"]) - 0.5, # range covering the bars
+ y0=exp_ref,
+ y1=exp_ref, # y-values for the horizontal line
+ line=dict(color="Red", width=2, dash="dash"),
+ layer="below",
+ )
+)
+
+fig.add_annotation(
+ go.layout.Annotation(
+ x=0.5,
+ xref="paper",
+ xanchor="center",
+ y=exp_ref,
+ yanchor="bottom",
+ text=f"Experiment: {exp_ref} kcal/mol [2]",
+ showarrow=False,
+ font=dict(
+ color="Red",
+ ),
+ )
+)
+
+fig.update_layout(
+ xaxis_title="Method [1] Lide, D. R. (Ed.). (2004). CRC handbook of chemistry and physics (Vol. 85). CRC press.",
+ yaxis_title="Reaction energy ๐ซH (kcal/mol)",
+)
+
+st.plotly_chart(fig)
+
+# Final reaction rate
+
+fig = go.Figure()
+
+df = df.sort_values("yield", ascending=True)
+
+fig.add_trace(
+ go.Bar(
+ x=df["yield"] * 100,
+ y=df["method"],
+ opacity=0.75,
+ orientation="h",
+ marker=dict(color=[method_color_mapping[method] for method in df["method"]]),
+ text=[f"{y:.2f} %" for y in df["yield"] * 100],
+ )
+)
+
+fig.update_layout(
+ title="Reaction yield (2H2 + O2 -> 2H2O, 64 units)",
+ xaxis_title="Yield (%)",
+ yaxis_title="Method",
+)
+
+st.plotly_chart(fig)
+
+# MD runtime speed
+
+fig = go.Figure()
+
+df = df.sort_values("steps_per_second", ascending=True)
+
+fig.add_trace(
+ go.Bar(
+ x=df["steps_per_second"],
+ y=df["method"],
+ opacity=0.75,
+ orientation="h",
+ marker=dict(color=[method_color_mapping[method] for method in df["method"]]),
+ text=df["steps_per_second"].round(1),
+ )
+)
+
+fig.update_layout(
+ title="MD runtime speed (on single A100 GPU)",
+ xaxis_title="Steps per second",
+ yaxis_title="Method",
+)
+
+st.plotly_chart(fig)
+
+# COM drift
+
+st.markdown("""### Center of mass drift
+
+The center of mass (COM) drift is a measure of the stability of the simulation. A well-behaved simulation should have a COM drift close to zero. The COM drift is calculated as the displacement of the COM of the system from the initial position.
+""")
+
+
+@st.cache_data
+def get_com_drifts(df):
+ df_exploded = df.explode(["timestep", "com_drifts"]).reset_index(drop=True)
+
+ # Convert the 'com_drifts' column (which are arrays) into separate columns for x, y, and z components
+ df_exploded[["com_drift_x", "com_drift_y", "com_drift_z"]] = pd.DataFrame(
+ df_exploded["com_drifts"].tolist(), index=df_exploded.index
+ )
+
+ # Drop the original 'com_drifts' column
+ df_flat = df_exploded.drop(columns=["com_drifts"])
+
+ df_flat["total_com_drift"] = np.sqrt(
+ df_flat["com_drift_x"] ** 2
+ + df_flat["com_drift_y"] ** 2
+ + df_flat["com_drift_z"] ** 2
+ )
+
+ return df_flat
+
+
+df_exploded = get_com_drifts(df)
+
+fig = go.Figure()
+
+for method in df_exploded["method"].unique():
+ row = df_exploded[df_exploded["method"] == method]
+ fig.add_trace(
+ go.Scatter(
+ x=row["timestep"],
+ y=row["total_com_drift"],
+ mode="lines",
+ name=method,
+ line=dict(
+ color=method_color_mapping[method],
+ # width=1
+ ),
+ marker=dict(color=method_color_mapping[method], size=3),
+ showlegend=True,
+ ),
+ )
+
+fig.update_yaxes(type="log")
+fig.update_layout(
+ xaxis_title="Timestep",
+ yaxis_title="Total COM drift (โซ)",
+)
+
+st.plotly_chart(fig)
+
+if "play" not in st.session_state:
+ st.session_state.play = False
+
+
+def toggle_playing():
+ st.session_state.play = not st.session_state.play
+
+
+# st.button(
+# "Play" if not st.session_state.play else "Pause",
+# type="primary" if not st.session_state.play else "secondary",
+# on_click=toggle_playing,
+# )
+
+increment = df["target_steps"].max() // 200
+
+if "time_range" not in st.session_state:
+ st.session_state.time_range = (0, increment)
+
+
+# @st.experimental_fragment(run_every=1e-3 if st.session_state.play else None)
+@st.experimental_fragment()
+def draw_com_drifts_plot():
+ if st.session_state.play:
+ start, end = st.session_state.time_range
+
+ end += increment
+
+ if end > df["target_steps"].max():
+ start = 0
+ end = 0
+
+ st.session_state.time_range = (start, end)
+
+ start_timestep, end_timestep = st.slider(
+ "Timestep",
+ min_value=0,
+ max_value=df["target_steps"].max(),
+ value=st.session_state.time_range,
+ key="time_range",
+ # on_change=check_range,
+ )
+
+ mask = (df_exploded["timestep"] >= start_timestep) & (
+ df_exploded["timestep"] <= end_timestep
+ )
+ df_filtered = df_exploded[mask]
+ df_filtered.sort_values(["method", "timestep"], inplace=True)
+
+ fig = px.line_3d(
+ data_frame=df_filtered,
+ x="com_drift_x",
+ y="com_drift_y",
+ z="com_drift_z",
+ labels={
+ "com_drift_x": "๐ซx (โซ)",
+ "com_drift_y": "๐ซy (โซ)",
+ "com_drift_z": "๐ซz (โซ)",
+ },
+ category_orders={"method": df_exploded["method"].unique()},
+ color_discrete_sequence=[
+ method_color_mapping[method] for method in df_exploded["method"].unique()
+ ],
+ color="method",
+ width=800,
+ height=800,
+ )
+
+ fig.update_layout(
+ scene=dict(
+ aspectmode="cube",
+ ),
+ legend=dict(
+ orientation="v",
+ x=0.95,
+ xanchor="right",
+ y=1,
+ yanchor="top",
+ bgcolor="rgba(0, 0, 0, 0)",
+ ),
+ )
+ fig.add_traces(
+ [
+ go.Scatter3d(
+ x=[0],
+ y=[0],
+ z=[0],
+ mode="markers",
+ marker=dict(size=3, color="white"),
+ name="origin",
+ ),
+ # add last point of each method and annotate the total drift
+ go.Scatter3d(
+ # df_filtered.groupby("method")["com_drift_x"].last(),
+ x=df_filtered.groupby("method")["com_drift_x"].last(),
+ y=df_filtered.groupby("method")["com_drift_y"].last(),
+ z=df_filtered.groupby("method")["com_drift_z"].last(),
+ mode="markers+text",
+ marker=dict(size=3, color="white", opacity=0.5),
+ text=df_filtered.groupby("method")["total_com_drift"].last().round(3),
+ # size=5,
+ name="total drifts",
+ textposition="top center",
+ ),
+ ]
+ )
+
+ st.plotly_chart(fig)
+
+
+draw_com_drifts_plot()
+
+
+st.markdown("""
+### References
+
+[1] Hasche, A., Navid, A., Krause, H., & Eckart, S. (2023). Experimental and numerical assessment of the effects of hydrogen admixtures on premixed methane-oxygen flames. Fuel, 352, 128964.
+
+[2] Lide, D. R. (Ed.). (2004). CRC handbook of chemistry and physics (Vol. 85). CRC press.
+""")
diff --git a/serve/tasks/eos_bulk.py b/serve/tasks/eos_bulk.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a9b8eb0b1a21c1aad3a4699bfccfc1940c958bd
--- /dev/null
+++ b/serve/tasks/eos_bulk.py
@@ -0,0 +1,248 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import plotly.colors as pcolors
+import plotly.graph_objects as go
+import streamlit as st
+from ase.db import connect
+from scipy import stats
+
+from mlip_arena.models import REGISTRY as MODELS
+
+DATA_DIR = Path("benchmarks/eos_bulk")
+
+st.markdown("""
+# Equation of state (EOS)
+""")
+
+# Control panels at the top
+st.markdown("### Methods")
+methods_container = st.container(border=True)
+
+valid_models = [
+ model
+ for model, metadata in MODELS.items()
+ if Path(__file__).stem in metadata.get("gpu-tasks", [])
+]
+
+# Model selection
+selected_models = methods_container.multiselect(
+ "Select Models",
+ options=valid_models,
+ default=valid_models
+)
+
+# Visualization settings
+st.markdown("### Visualization settings")
+vis = st.container(border=True)
+
+# Column settings
+ncols = vis.select_slider("Number of columns", options=[1, 2, 3, 4], value=2)
+
+# Color palette selection
+all_attributes = dir(pcolors.qualitative)
+color_palettes = {
+ attr: getattr(pcolors.qualitative, attr)
+ for attr in all_attributes
+ if isinstance(getattr(pcolors.qualitative, attr), list)
+}
+color_palettes.pop("__all__", None)
+
+palette_names = list(color_palettes.keys())
+palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
+color_sequence = color_palettes[palette_name]
+
+# Stop execution if no models selected
+if not selected_models:
+ st.warning("Please select at least one model to visualize.")
+ st.stop()
+
+
+def load_wbm_structures():
+ """
+ Load the WBM structures from a ASE DB file.
+ """
+ with connect(DATA_DIR.parent / "wbm_structures.db") as db:
+ for row in db.select():
+ yield row.toatoms(add_additional_information=True)
+
+
+@st.cache_data
+def generate_dataframe(model_name):
+ fpath = DATA_DIR / f"{model_name}.parquet"
+ if not fpath.exists():
+ return pd.DataFrame() # Return empty dataframe instead of using continue
+
+ df_raw_results = pd.read_parquet(fpath)
+
+ df_analyzed = pd.DataFrame(
+ columns=[
+ "model",
+ "structure",
+ "formula",
+ "volume-ratio",
+ "energy-delta-per-atom",
+ "energy-delta-per-volume-b0",
+ "energy-diff-flip-times",
+ "tortuosity",
+ "spearman-compression-energy",
+ "spearman-compression-derivative",
+ "spearman-tension-energy",
+ "missing",
+ ]
+ )
+
+ for wbm_struct in load_wbm_structures():
+ structure_id = wbm_struct.info["key_value_pairs"]["wbm_id"]
+
+ try:
+ results = df_raw_results.loc[df_raw_results["id"] == structure_id]
+ b0 = results["b0"].values[0]
+ # vol0 = results["v0"].values[0]
+ results = results["eos"].values[0]
+ es = np.array(results["energies"])
+ vols = np.array(results["volumes"])
+
+
+ indices = np.argsort(vols)
+ vols = vols[indices]
+ es = es[indices]
+
+ imine = len(es) // 2
+ # min_center_val = np.min(es[imid - 1 : imid + 2])
+ # imine = np.where(es == min_center_val)[0][0]
+ emin = es[imine]
+ vol0 = vols[imine]
+
+ interpolated_volumes = [
+ (vols[i] + vols[i + 1]) / 2 for i in range(len(vols) - 1)
+ ]
+ ediff = np.diff(es)
+ ediff_sign = np.sign(ediff)
+ mask = ediff_sign != 0
+ ediff = ediff[mask]
+ ediff_sign = ediff_sign[mask]
+ ediff_flip = np.diff(ediff_sign) != 0
+
+ etv = np.sum(np.abs(np.diff(es)))
+
+ data = {
+ "model": model_name,
+ "structure": structure_id,
+ "formula": wbm_struct.get_chemical_formula(),
+ "missing": False,
+ "volume-ratio": vols / vol0,
+ "energy-delta-per-atom": (es - emin) / len(wbm_struct),
+ "energy-diff-flip-times": np.sum(ediff_flip).astype(int),
+ "energy-delta-per-volume-b0": (es - emin) / (vol0 * b0),
+ "tortuosity": etv / (abs(es[0] - emin) + abs(es[-1] - emin)),
+ "spearman-compression-energy": stats.spearmanr(
+ vols[:imine], es[:imine]
+ ).statistic,
+ "spearman-compression-derivative": stats.spearmanr(
+ interpolated_volumes[:imine], ediff[:imine]
+ ).statistic,
+ "spearman-tension-energy": stats.spearmanr(
+ vols[imine:], es[imine:]
+ ).statistic,
+ }
+
+ except Exception:
+ data = {
+ "model": model_name,
+ "structure": structure_id,
+ "formula": wbm_struct.get_chemical_formula(),
+ "missing": True,
+ "volume-ratio": None,
+ "energy-delta-per-atom": None,
+ "energy-delta-per-volume-b0": None,
+ "energy-diff-flip-times": None,
+ "tortuosity": None,
+ "spearman-compression-energy": None,
+ "spearman-compression-derivative": None,
+ "spearman-tension-energy": None,
+ }
+
+ df_analyzed = pd.concat([df_analyzed, pd.DataFrame([data])], ignore_index=True)
+
+ return df_analyzed
+
+
+@st.cache_data
+def get_plots(selected_models):
+ """Generate one plot per model with all structures (legend disabled for each structure)."""
+ figs = []
+
+ for model_name in selected_models:
+
+ fpath = DATA_DIR / f"{model_name}_processed.parquet"
+ if not fpath.exists():
+ df = generate_dataframe(model_name)
+ else:
+ df = pd.read_parquet(fpath)
+
+ if len(df) == 0:
+ continue
+
+ fig = go.Figure()
+ valid_structures = []
+ for i, (_, row) in enumerate(df.iterrows()):
+ structure_id = row["structure"]
+ formula = row.get("formula", "")
+ if isinstance(row["volume-ratio"], list | np.ndarray) and isinstance(
+ row["energy-delta-per-volume-b0"], list | np.ndarray
+ ):
+ vol_strain = row["volume-ratio"]
+ energy_delta = row["energy-delta-per-volume-b0"]
+ color = color_sequence[i % len(color_sequence)]
+ fig.add_trace(
+ go.Scatter(
+ x=vol_strain,
+ y=energy_delta,
+ mode="lines",
+ name=f"{structure_id}",
+ showlegend=False,
+ line=dict(color=color),
+ hoverlabel=dict(bgcolor=color, font=dict(color="black")),
+ hovertemplate=(
+ structure_id + " "
+ "Formula: " + str(formula) + " "
+ "Volume ratio V/Vโ: %{x:.3f} "
+ "ฮE/(BVโ): %{y:.3f} eV/atom "
+ ""
+ ),
+
+ )
+ )
+ valid_structures.append(structure_id)
+
+ # if valid_structures:
+ fig.update_layout(
+ title=f"{model_name} ({len(valid_structures)} / {len(df)} structures)",
+ xaxis_title="Volume ratio V/Vโ",
+ yaxis_title="Relative energy ฮE/(BVโ)",
+ height=500,
+ showlegend=False, # Disable legend for the whole plot
+ yaxis=dict(range=[-0.02, 0.1]), # Set y-axis limits
+ )
+ fig.add_vline(x=1, line_dash="dash", line_color="gray", opacity=0.7)
+ figs.append((model_name, fig, valid_structures))
+
+ return figs
+
+
+# Generate all plots
+all_plots = get_plots(selected_models)
+
+# Display plots in the specified column layout
+if all_plots:
+ for i, (model_name, fig, structures) in enumerate(all_plots):
+ if i % ncols == 0:
+ cols = st.columns(ncols)
+ cols[i % ncols].plotly_chart(fig, use_container_width=True)
+
+ # Display number of structures in this plot
+ # cols[i % ncols].caption(f"{len(structures)} / 1000 structures")
+else:
+ st.warning("No data available for the selected models.")
diff --git a/serve/tasks/homonuclear-diatomics.py b/serve/tasks/homonuclear-diatomics.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0058192dd51294797664005089633ae7fc862bb
--- /dev/null
+++ b/serve/tasks/homonuclear-diatomics.py
@@ -0,0 +1,285 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import plotly.colors as pcolors
+import plotly.graph_objects as go
+import streamlit as st
+from ase.data import chemical_symbols
+from plotly.subplots import make_subplots
+
+from mlip_arena.models import REGISTRY
+
+st.markdown(
+ """
+# Homonuclear Diatomics
+
+Homonuclear diatomics are molecules composed of two atoms of the same element.
+The potential energy curves of homonuclear diatomics are the most fundamental interactions between atoms in quantum chemistry.
+"""
+)
+
+st.markdown("### Methods")
+container = st.container(border=True)
+valid_models = [
+ model
+ for model, metadata in REGISTRY.items()
+ if Path(__file__).stem in metadata.get("gpu-tasks", [])
+]
+mlip_methods = container.multiselect(
+ "MLIPs",
+ valid_models,
+ ["MACE-MP(M)", "MatterSim", "SevenNet", "ORBv2", "eqV2(OMat)", "ANI2x", "eSEN"],
+)
+dft_methods = container.multiselect("DFT Methods", ["PBE"], ["PBE"])
+
+container.info(
+ "PBE energies and forces are provided __only__ for reference. Due to the known convergence issue of plane-wave DFT with diatomic molecules and different dataset the models might be trained on, comparing models with PBE is not rigorous and thus these metrics are excluded from rank aggregation.",
+ icon=":material/warning:",
+)
+
+st.markdown("### Settings")
+vis = st.container(border=True)
+energy_plot = vis.checkbox("Show energy curves", value=True)
+force_plot = vis.checkbox("Show force curves", value=False)
+ncols = vis.select_slider("Number of columns", options=[1, 2, 3, 4], value=2)
+
+# Get all attributes from pcolors.qualitative
+all_attributes = dir(pcolors.qualitative)
+color_palettes = {
+ attr: getattr(pcolors.qualitative, attr)
+ for attr in all_attributes
+ if isinstance(getattr(pcolors.qualitative, attr), list)
+}
+color_palettes.pop("__all__", None)
+
+palette_names = list(color_palettes.keys())
+palette_colors = list(color_palettes.values())
+
+palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
+
+color_sequence = color_palettes[palette_name] # type: ignore
+if not mlip_methods and not dft_methods:
+ st.stop()
+
+
+@st.cache_data
+def get_data(mlip_methods, dft_methods):
+ DATA_DIR = Path("mlip_arena/tasks/diatomics")
+
+ dfs = [
+ pd.read_json(
+ DATA_DIR / REGISTRY[method]["family"] / "homonuclear-diatomics.json"
+ )
+ for method in mlip_methods
+ ]
+ dfs.extend(
+ [
+ pd.read_json(DATA_DIR / "vasp" / "homonuclear-diatomics.json")
+ # for method in dft_methods
+ ]
+ )
+ df = pd.concat(dfs, ignore_index=True)
+ df.drop_duplicates(inplace=True, subset=["name", "method"])
+ return df
+
+
+df = get_data(mlip_methods, dft_methods)
+
+method_color_mapping = {
+ method: color_sequence[i % len(color_sequence)]
+ for i, method in enumerate(df["method"].unique())
+}
+
+
+@st.cache_data
+def get_plots(df, energy_plot: bool, force_plot: bool, method_color_mapping: dict):
+ figs = []
+
+ for i, symbol in enumerate(chemical_symbols[1:]):
+ rows = df[df["name"] == symbol + symbol]
+
+ if rows.empty:
+ continue
+
+ fig = make_subplots(specs=[[{"secondary_y": True}]])
+
+ elo, flo = float("inf"), float("inf")
+
+ for j, method in enumerate(rows["method"].unique()):
+ if method not in mlip_methods and method not in dft_methods:
+ continue
+ row = rows[rows["method"] == method].iloc[0]
+
+ rs = np.array(row["R"])
+ es = np.array(row["E"])
+ fs = np.array(row["F"])
+
+ rs = np.array(rs)
+ ind = np.argsort(rs)
+ es = np.array(es)
+ fs = np.array(fs)
+
+ rs = rs[ind]
+ es = es[ind]
+ fs = fs[ind]
+
+ # if method not in ["PBE"]:
+ es = es - es[-1]
+
+ # if method in ["PBE"]:
+ # xs = np.linspace(rs.min() * 0.99, rs.max() * 1.01, int(5e2))
+ # else:
+ xs = rs
+
+ if energy_plot:
+ # if "GPAW" in method:
+ # cs = CubicSpline(rs, es)
+ # ys = cs(xs)
+ # else:
+ ys = es
+
+ elo = min(elo, max(ys.min() * 1.2, -15), -1)
+
+ if method in ["PBE"]:
+ fig.add_trace(
+ go.Scatter(
+ x=xs,
+ y=ys,
+ mode="markers",
+ line=dict(
+ color=method_color_mapping[method],
+ width=3,
+ ),
+ name=method,
+ ),
+ secondary_y=False,
+ )
+ # xs = np.linspace(rs.min() * 0.99, rs.max() * 1.01, int(5e2))
+ # cs = CubicSpline(rs, es)
+ # ys = cs(xs)
+ # fig.add_trace(
+ # go.Scatter(
+ # x=xs,
+ # y=ys,
+ # mode="lines",
+ # line=dict(
+ # color=method_color_mapping[method],
+ # width=3,
+ # ),
+ # name=method,
+ # showlegend=False,
+ # ),
+ # secondary_y=False,
+ # )
+ else:
+ fig.add_trace(
+ go.Scatter(
+ x=xs,
+ y=ys,
+ mode="lines",
+ line=dict(
+ color=method_color_mapping[method],
+ width=3,
+ ),
+ name=method,
+ ),
+ secondary_y=False,
+ )
+
+ # if force_plot and method not in ["PBE"]:
+ if force_plot:
+ ys = fs
+
+ flo = min(flo, max(ys.min() * 1.2, -50))
+
+ if method in ["PBE"]:
+ fig.add_trace(
+ go.Scatter(
+ x=xs,
+ y=ys,
+ mode="lines+markers",
+ line=dict(
+ color=method_color_mapping[method],
+ width=2,
+ dash="dashdot",
+ ),
+ name=method,
+ showlegend=not energy_plot,
+ ),
+ secondary_y=True,
+ )
+ else:
+ fig.add_trace(
+ go.Scatter(
+ x=xs,
+ y=ys,
+ mode="lines",
+ line=dict(
+ color=method_color_mapping[method],
+ width=2,
+ dash="dashdot",
+ ),
+ name=method,
+ showlegend=not energy_plot,
+ ),
+ secondary_y=True,
+ )
+
+ name = f"{symbol}-{symbol}"
+
+ fig.update_layout(
+ showlegend=True,
+ legend=dict(
+ orientation="h",
+ x=1.0,
+ xanchor="right",
+ y=1,
+ yanchor="top",
+ bgcolor="rgba(0, 0, 0, 0)",
+ # traceorder='reversed',
+ entrywidth=0.4,
+ entrywidthmode="fraction",
+ ),
+ title_text=f"{name}",
+ title_x=0.5,
+ )
+
+ # Set x-axis title
+ fig.update_xaxes(title_text="Distance [ร ]")
+
+ # Set y-axes titles
+ if energy_plot:
+ fig.update_layout(
+ yaxis=dict(
+ title=dict(text="Energy [eV]"),
+ side="left",
+ range=[elo, 2.0 * (abs(elo))],
+ )
+ )
+
+ if force_plot:
+ fig.update_layout(
+ yaxis2=dict(
+ title=dict(text="Force [eV/ร ]"),
+ side="right",
+ range=[flo, 1.0 * abs(flo)],
+ overlaying="y",
+ tickmode="sync",
+ ),
+ )
+
+ # cols[i % ncols].plotly_chart(fig, use_container_width=True)
+
+ figs.append(fig)
+
+ return figs
+ # fig.write_image(format='svg', file=img_dir / f"{name}.svg")
+
+
+figs = get_plots(df, energy_plot, force_plot, method_color_mapping)
+
+for i, fig in enumerate(figs):
+ if i % ncols == 0:
+ cols = st.columns(ncols)
+ cols[i % ncols].plotly_chart(fig, use_container_width=True)
diff --git a/serve/tasks/stability.py b/serve/tasks/stability.py
new file mode 100644
index 0000000000000000000000000000000000000000..575dccca0961e42ea6cacbcc9adb05b6e4b62b98
--- /dev/null
+++ b/serve/tasks/stability.py
@@ -0,0 +1,228 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import plotly.colors as pcolors
+import plotly.express as px
+import plotly.graph_objects as go
+import streamlit as st
+from scipy.optimize import curve_fit
+
+from mlip_arena.models import REGISTRY
+
+DATA_DIR = Path("mlip_arena/tasks/stability")
+
+
+st.markdown("""
+# High Pressure Stability
+
+Stable and accurate molecular dynamics (MD) simulations are important for understanding the properties of matters.
+However, many MLIPs have unphysical potential energy surface (PES) at the short-range interatomic distances or under many-body effect. These are often manifested as softened repulsion and hole in the PES and can lead to incorrect and sampling of the phase space.
+
+Here, we analyze the stability of the MD simulations under high pressure conditions by gradually increasing the pressure from 0 to 1000 GPa at 300K until the system crashes or completes 100 ps trajectory. This benchmark also explores faster the far-from-equilibrium dynamics of the system and the durability of the MLIPs under extreme conditions.
+""")
+
+st.markdown("### Methods")
+container = st.container(border=True)
+valid_models = [
+ model
+ for model, metadata in REGISTRY.items()
+ if Path(__file__).stem in metadata.get("gpu-tasks", [])
+]
+
+models = container.multiselect(
+ "MLIPs", valid_models, ["MACE-MP(M)", "CHGNet", "ORB", "SevenNet"]
+)
+
+st.markdown("### Settings")
+vis = st.container(border=True)
+# Get all attributes from pcolors.qualitative
+all_attributes = dir(pcolors.qualitative)
+color_palettes = {
+ attr: getattr(pcolors.qualitative, attr)
+ for attr in all_attributes
+ if isinstance(getattr(pcolors.qualitative, attr), list)
+}
+color_palettes.pop("__all__", None)
+
+palette_names = list(color_palettes.keys())
+palette_colors = list(color_palettes.values())
+
+palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
+
+color_sequence = color_palettes[palette_name]
+
+if not models:
+ st.stop()
+
+
+@st.cache_data
+def get_data(models):
+ families = [REGISTRY[str(model)]["family"] for model in models]
+
+ dfs = [
+ pd.read_json(DATA_DIR / family.lower() / "chloride-salts.json")
+ for family in families
+ ]
+ df = pd.concat(dfs, ignore_index=True)
+ df.drop_duplicates(inplace=True, subset=["material_id", "formula", "method"])
+
+ return df
+
+
+df = get_data(models)
+
+method_color_mapping = {
+ method: color_sequence[i % len(color_sequence)]
+ for i, method in enumerate(df["method"].unique())
+}
+
+###
+
+# Determine the bin edges for the entire dataset to keep them consistent across groups
+
+max_steps = df["total_steps"].max()
+max_target_steps = df["target_steps"].max()
+
+bins = np.append(np.arange(0, max_steps + 1, max_steps // 10), max_target_steps)
+bin_labels = [f"{bins[i]}-{bins[i+1]}" for i in range(len(bins) - 1)]
+
+num_bins = len(bin_labels)
+colormap = px.colors.sequential.YlOrRd_r
+indices = np.linspace(0, len(colormap) - 1, num_bins, dtype=int)
+bin_colors = [colormap[i] for i in indices]
+
+# Initialize a dictionary to hold the counts for each method and bin range
+counts_per_method = {method: [0] * len(bin_labels) for method in df["method"].unique()}
+
+
+# Populate the dictionary with counts
+for method, group in df.groupby("method"):
+ counts, _ = np.histogram(group["total_steps"], bins=bins)
+ counts_per_method[method] = counts
+
+# Sort the dictionary by the percentage of the last bin
+counts_per_method = {
+ k: v
+ for k, v in sorted(
+ counts_per_method.items(), key=lambda item: item[1][-1] / sum(item[1])
+ )
+}
+
+
+count_or_percetange = st.toggle("show counts", False)
+
+
+@st.experimental_fragment()
+def plot_md_steps(counts_per_method, count_or_percetange):
+ """Plot the distribution of the total number of MD steps before crash or completion."""
+ # Create a figure
+ fig = go.Figure()
+
+ # Add a bar for each bin range across all methods
+ for i, bin_label in enumerate(bin_labels):
+ for method, counts in counts_per_method.items():
+ fig.add_trace(
+ go.Bar(
+ # name=method, # This will be the legend entry
+ x=[counts[i] / counts.sum() * 100]
+ if not count_or_percetange
+ else [counts[i]],
+ y=[method], # Method as the y-axis category
+ # name=bin_label,
+ orientation="h", # Horizontal bars
+ marker=dict(
+ color=bin_colors[i],
+ line=dict(color="rgb(248, 248, 249)", width=1),
+ ),
+ text=f"{bin_label}: {counts[i]/counts.sum()*100:.0f}%",
+ width=0.5,
+ )
+ )
+
+ # Update the layout to stack the bars
+ fig.update_layout(
+ barmode="stack", # Stack the bars
+ title="Total MD steps (before crash or completion)",
+ xaxis_title="Percentage (%)" if not count_or_percetange else "Count",
+ yaxis_title="Method",
+ showlegend=False,
+ )
+
+ st.plotly_chart(fig)
+
+
+plot_md_steps(counts_per_method, count_or_percetange)
+
+st.caption(
+"""
+The histogram shows the distribution of the total number of MD steps before the system crashes or completes the trajectory. :red[The color of the bins indicates the number of steps in the bin]. :blue[The height of the bars indicates the number or percentage of each bin among all the runs].
+"""
+)
+
+###
+
+st.markdown(
+"""
+## Inference speed
+
+The inference speed of the MLIPs is crucial for the high-throughput virutal screening. Under high pressure conditions, the atoms often move faster and closer to each other, which increases the size of neighbor list and local graph construction and hence slows down the inference speed.
+"""
+)
+
+
+def func(x, a, n):
+ return a * x ** (-n)
+
+
+@st.experimental_fragment()
+def plot_speed(df, method_color_mapping):
+ """Plot the inference speed as a function of the number of atoms."""
+ fig = px.scatter(
+ df,
+ x="natoms",
+ y="steps_per_second",
+ color="method",
+ size="total_steps",
+ hover_data=["material_id", "formula"],
+ color_discrete_map=method_color_mapping,
+ # trendline="ols",
+ # trendline_options=dict(log_x=True),
+ log_x=True,
+ # log_y=True,
+ # range_y=[1, 1e2],
+ range_x=[df["natoms"].min() * 0.9, df["natoms"].max() * 1.1],
+ # range_x=[1e3, 1e2],
+ title="Inference speed (on single A100 GPU)",
+ labels={"steps_per_second": "Steps per second", "natoms": "Number of atoms"},
+ )
+
+ x = np.linspace(df["natoms"].min(), df["natoms"].max(), 100)
+
+ for method, data in df.groupby("method"):
+ data.dropna(subset=["steps_per_second"], inplace=True)
+ popt, pcov = curve_fit(func, data["natoms"], data["steps_per_second"])
+
+ fig.add_trace(
+ go.Scatter(
+ x=x,
+ y=func(x, *popt),
+ mode="lines",
+ # name='Fit',
+ line=dict(color=method_color_mapping[method], width=3),
+ showlegend=False,
+ name=f"{popt[0]:.2f}N^{-popt[1]:.2f}",
+ hovertext=f"{popt[0]:.2f}N^{-popt[1]:.2f}",
+ )
+ )
+
+ st.plotly_chart(fig)
+
+
+plot_speed(df, method_color_mapping)
+
+st.caption(
+"""
+The plot shows the inference speed (steps per second) as a function of the number of atoms in the system. :red[The size of the points is proportional to the total number of steps in the MD trajectory before crash or completion (~49990)]. :blue[The lines show the fit of the data to the power law function $a N^{-n}$], where $N$ is the number of atoms and $a$ and $n$ are the fit parameters.
+"""
+)
diff --git a/serve/tasks/thermal-conductivity.py b/serve/tasks/thermal-conductivity.py
new file mode 100644
index 0000000000000000000000000000000000000000..3148be19ef3515e65efa4e3f6a8c1268406c9a33
--- /dev/null
+++ b/serve/tasks/thermal-conductivity.py
@@ -0,0 +1,48 @@
+
+import pandas as pd
+import streamlit as st
+
+from mlip_arena import PKG_DIR
+
+
+st.markdown(
+"""
+# Thermal Conductivity
+
+Compared to Pรณta, Ahlawat, Csรกnyi, and Simoncelli, [arXiv:2408.00755v4](https://arxiv.org/abs/2408.00755), the relaxation protocol has been updated and unified for all the models. The relaxation is a combination of sequential vc-relax (changes cell and atom positions) and relax (changes atom positions only). Each relaxation stage has a maximum number of 300 steps, and consist of a single FrechetCellFilter relaxation with force threshold =1e-4 eV/Ang. To preserve crystal symmetry, unit-cell angles are not allowed to change. This unified protocol gives the same SRME reported in [arXiv:2408.00755v4](https://arxiv.org/abs/2408.00755) for all the models but M3GNet. In M3GNet this updated relaxation protocol gives SRME = 1.412, slightly smaller than the value 1.469 that was obtained with the non-unified relaxation protocol in [arXiv:2408.00755v4](https://arxiv.org/abs/2408.00755).
+
+**SRME** is the Symmetric Relative Mean Error, defined as the mean of the absolute values of the relative errors of the predictions. Here, it is used to quantify the error on microscopic single-phonon conductivity:
+
+$$
+\\text{SRME}[\\left\lbrace\\mathcal{K}({\\mathbf{q},s)}\\right\\rbrace] = \\frac{2}{N_qV}\\frac{\\sum_{\\mathbf{q}s}|\\mathcal{K}_{\\text{MLIP}}(\\mathbf{q},s) - \\mathcal{K}_{\\text{DFT}}(\\mathbf{q},s)|}{\\kappa_{\\text{MLIP}} + \\kappa_{\\text{DFT}}}
+$$
+"""
+)
+
+DATA_DIR = PKG_DIR / "tasks" / "thermal-conductivity"
+
+table = pd.read_csv(DATA_DIR / "wte.csv")
+
+table.rename(
+ columns={
+ "method": "Model",
+ "srme": "SRME[๐ ]",
+ },
+ inplace=True,
+)
+
+table.set_index("Model", inplace=True)
+
+table.sort_values(["SRME[๐ ]"], ascending=True, inplace=True)
+
+s = (
+ table.style.background_gradient(
+ cmap="Reds", subset=["SRME[๐ ]"]
+ )
+ .format("{:.3f}", subset=["SRME[๐ ]"])
+)
+
+st.dataframe(
+ s,
+ use_container_width=True,
+)
\ No newline at end of file
diff --git a/serve/tasks/wbm_ev.py b/serve/tasks/wbm_ev.py
new file mode 100644
index 0000000000000000000000000000000000000000..c68b35b5a2f5281683571996053a94b924a7722b
--- /dev/null
+++ b/serve/tasks/wbm_ev.py
@@ -0,0 +1,243 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import plotly.colors as pcolors
+import plotly.graph_objects as go
+import streamlit as st
+from ase.db import connect
+from scipy import stats
+
+from mlip_arena.models import REGISTRY as MODELS
+
+DATA_DIR = Path("benchmarks/wbm_ev")
+
+st.markdown("""
+# Energy-volume scans
+""")
+
+# Control panels at the top
+st.markdown("### Methods")
+methods_container = st.container(border=True)
+
+# Get valid models that support wbm_ev
+valid_models = [
+ model
+ for model, metadata in MODELS.items()
+ if Path(__file__).stem in metadata.get("gpu-tasks", [])
+]
+
+# Model selection
+selected_models = methods_container.multiselect(
+ "Select Models",
+ options=valid_models,
+ default=valid_models
+)
+
+# Visualization settings
+st.markdown("### Visualization Settings")
+vis = st.container(border=True)
+
+# Column settings
+ncols = vis.select_slider("Number of columns", options=[1, 2, 3, 4], value=2)
+
+# Color palette selection
+all_attributes = dir(pcolors.qualitative)
+color_palettes = {
+ attr: getattr(pcolors.qualitative, attr)
+ for attr in all_attributes
+ if isinstance(getattr(pcolors.qualitative, attr), list)
+}
+color_palettes.pop("__all__", None)
+
+palette_names = list(color_palettes.keys())
+palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
+color_sequence = color_palettes[palette_name]
+
+# Stop execution if no models selected
+if not selected_models:
+ st.warning("Please select at least one model to visualize.")
+ st.stop()
+
+
+def load_wbm_structures():
+ """
+ Load the WBM structures from a ASE DB file.
+ """
+ with connect(DATA_DIR.parent / "wbm_structures.db") as db:
+ for row in db.select():
+ yield row.toatoms(add_additional_information=True)
+
+
+@st.cache_data
+def generate_dataframe(model_name):
+ fpath = DATA_DIR / f"{model_name}.parquet"
+ if not fpath.exists():
+ return pd.DataFrame() # Return empty dataframe instead of using continue
+
+ df_raw_results = pd.read_parquet(fpath)
+
+ df_analyzed = pd.DataFrame(
+ columns=[
+ "model",
+ "structure",
+ "formula",
+ "volume-ratio",
+ "energy-delta-per-atom",
+ "energy-diff-flip-times",
+ "tortuosity",
+ "spearman-compression-energy",
+ "spearman-compression-derivative",
+ "spearman-tension-energy",
+ "missing",
+ ]
+ )
+
+ for wbm_struct in load_wbm_structures():
+ structure_id = wbm_struct.info["key_value_pairs"]["wbm_id"]
+
+ try:
+ results = df_raw_results.loc[df_raw_results["id"] == structure_id]
+ results = results["eos"].values[0]
+ es = np.array(results["energies"])
+ vols = np.array(results["volumes"])
+ vol0 = wbm_struct.get_volume()
+
+ indices = np.argsort(vols)
+ vols = vols[indices]
+ es = es[indices]
+
+ imine = len(es) // 2
+ # min_center_val = np.min(es[imid - 1 : imid + 2])
+ # imine = np.where(es == min_center_val)[0][0]
+ emin = es[imine]
+
+ interpolated_volumes = [
+ (vols[i] + vols[i + 1]) / 2 for i in range(len(vols) - 1)
+ ]
+ ediff = np.diff(es)
+ ediff_sign = np.sign(ediff)
+ mask = ediff_sign != 0
+ ediff = ediff[mask]
+ ediff_sign = ediff_sign[mask]
+ ediff_flip = np.diff(ediff_sign) != 0
+
+ etv = np.sum(np.abs(np.diff(es)))
+
+ data = {
+ "model": model_name,
+ "structure": structure_id,
+ "formula": wbm_struct.get_chemical_formula(),
+ "missing": False,
+ "volume-ratio": vols / vol0,
+ "energy-delta-per-atom": (es - emin) / len(wbm_struct),
+ "energy-diff-flip-times": np.sum(ediff_flip).astype(int),
+ "tortuosity": etv / (abs(es[0] - emin) + abs(es[-1] - emin)),
+ "spearman-compression-energy": stats.spearmanr(
+ vols[:imine], es[:imine]
+ ).statistic,
+ "spearman-compression-derivative": stats.spearmanr(
+ interpolated_volumes[:imine], ediff[:imine]
+ ).statistic,
+ "spearman-tension-energy": stats.spearmanr(
+ vols[imine:], es[imine:]
+ ).statistic,
+ }
+
+ except Exception:
+ data = {
+ "model": model_name,
+ "structure": structure_id,
+ "formula": wbm_struct.get_chemical_formula(),
+ "missing": True,
+ "volume-ratio": None,
+ "energy-delta-per-atom": None,
+ "energy-diff-flip-times": None,
+ "tortuosity": None,
+ "spearman-compression-energy": None,
+ "spearman-compression-derivative": None,
+ "spearman-tension-energy": None,
+ }
+
+ df_analyzed = pd.concat([df_analyzed, pd.DataFrame([data])], ignore_index=True)
+
+ return df_analyzed
+
+
+@st.cache_data
+def get_plots(selected_models):
+ """Generate one plot per model with all structures (legend disabled for each structure)."""
+ figs = []
+
+ for model_name in selected_models:
+
+ fpath = DATA_DIR / f"{model_name}_processed.parquet"
+ if not fpath.exists():
+ df = generate_dataframe(model_name)
+ else:
+ df = pd.read_parquet(fpath)
+
+ if len(df) == 0:
+ continue
+
+ fig = go.Figure()
+ valid_structures = []
+ for i, (_, row) in enumerate(df.iterrows()):
+ structure_id = row["structure"]
+ formula = row.get("formula", "")
+ if isinstance(row["volume-ratio"], list | np.ndarray) and isinstance(
+ row["energy-delta-per-atom"], list | np.ndarray
+ ):
+ vol_strain = row["volume-ratio"]
+ energy_delta = row["energy-delta-per-atom"]
+ color = color_sequence[i % len(color_sequence)]
+ fig.add_trace(
+ go.Scatter(
+ x=vol_strain,
+ y=energy_delta,
+ mode="lines",
+ name=f"{structure_id}",
+ showlegend=False,
+ line=dict(color=color),
+ hoverlabel=dict(bgcolor=color, font=dict(color="black")),
+ hovertemplate=(
+ structure_id + " "
+ "Formula: " + str(formula) + " "
+ "Volume ratio V/Vโ: %{x:.3f} "
+ "ฮEnergy: %{y:.3f} eV/atom "
+ ""
+ ),
+
+ )
+ )
+ valid_structures.append(structure_id)
+
+ # if valid_structures:
+ fig.update_layout(
+ title=f"{model_name} ({len(valid_structures)} / {len(df)} structures)",
+ xaxis_title="Volume ratio V/Vโ",
+ yaxis_title="Relative energy E - Eโ (eV/atom)",
+ height=500,
+ showlegend=False, # Disable legend for the whole plot
+ yaxis=dict(range=[-1, 15]), # Set y-axis limits
+ )
+ fig.add_vline(x=1, line_dash="dash", line_color="gray", opacity=0.7)
+ figs.append((model_name, fig, valid_structures))
+
+ return figs
+
+
+# Generate all plots
+all_plots = get_plots(selected_models)
+
+# Display plots in the specified column layout
+if all_plots:
+ for i, (model_name, fig, structures) in enumerate(all_plots):
+ if i % ncols == 0:
+ cols = st.columns(ncols)
+ cols[i % ncols].plotly_chart(fig, use_container_width=True)
+
+ # Display number of structures in this plot
+ # cols[i % ncols].caption(f"{len(structures)} / 1000 structures")
+else:
+ st.warning("No data available for the selected models.")
diff --git a/serve/tools/ptable.py b/serve/tools/ptable.py
new file mode 100644
index 0000000000000000000000000000000000000000..e73dcf9ce02444271be0029597b1838d486fc6e5
--- /dev/null
+++ b/serve/tools/ptable.py
@@ -0,0 +1,158 @@
+
+
+# NOTE: https://stackoverflow.com/questions/77062368/streamlit-bokeh-event-callback-to-get-clicked-values
+# Taptool: https://docs.bokeh.org/en/2.4.2/docs/reference/models/tools.html#taptool
+
+import streamlit as st
+from bokeh.plotting import figure
+from bokeh.plotting import figure, show
+from bokeh.sampledata.periodic_table import elements
+from bokeh.transform import dodge, factor_cmap
+
+import streamlit as st
+from bokeh.plotting import figure
+from bokeh.models import ColumnDataSource, CustomJS, TapTool
+from bokeh.sampledata.periodic_table import elements
+from bokeh.transform import dodge, factor_cmap
+
+
+periods = ["I", "II", "III", "IV", "V", "VI", "VII"]
+groups = [str(x) for x in range(1, 19)]
+
+df = elements.copy()
+df["atomic mass"] = df["atomic mass"].astype(str)
+df["group"] = df["group"].astype(str)
+df["period"] = [periods[x-1] for x in df.period]
+df = df[df.group != "-"]
+df = df[df.symbol != "Lr"]
+df = df[df.symbol != "Lu"]
+
+cmap = {
+ "alkali metal" : "#a6cee3",
+ "alkaline earth metal" : "#1f78b4",
+ "metal" : "#d93b43",
+ "halogen" : "#999d9a",
+ "metalloid" : "#e08d49",
+ "noble gas" : "#eaeaea",
+ "nonmetal" : "#f1d4Af",
+ "transition metal" : "#599d7A",
+}
+
+TOOLTIPS = [
+ ("Name", "@name"),
+ ("Atomic number", "@{atomic number}"),
+ ("Atomic mass", "@{atomic mass}"),
+ ("Type", "@metal"),
+ ("CPK color", "$color[hex, swatch]:CPK"),
+ ("Electronic configuration", "@{electronic configuration}"),
+]
+
+p = figure(title="Periodic Table (omitting LA and AC Series)", width=1000, height=450,
+ x_range=groups, y_range=list(reversed(periods)),
+ tools="hover,tap", toolbar_location=None, tooltips=TOOLTIPS)
+
+# Convert DataFrame to ColumnDataSource
+df["selected"] = False
+source = ColumnDataSource(df)
+
+r = p.rect("group", "period", 0.95, 0.95, source=source, fill_alpha=0.6,
+ legend_field="metal",
+ color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys())),
+ selection_color="firebrick", selection_alpha=0.9)
+
+
+# r = p.rect("group", "period", 0.95, 0.95, source=df, fill_alpha=0.6, legend_field="metal",
+# color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys())))
+
+text_props = dict(source=df, text_align="left", text_baseline="middle")
+
+x = dodge("group", -0.4, range=p.x_range)
+
+p.text(x=x, y="period", text="symbol", text_font_style="bold", **text_props)
+
+p.text(x=x, y=dodge("period", 0.3, range=p.y_range), text="atomic number",
+ text_font_size="11px", **text_props)
+
+p.text(x=x, y=dodge("period", -0.35, range=p.y_range), text="name",
+ text_font_size="7px", **text_props)
+
+p.text(x=x, y=dodge("period", -0.2, range=p.y_range), text="atomic mass",
+ text_font_size="7px", **text_props)
+
+p.text(x=["3", "3"], y=["VI", "VII"], text=["LA", "AC"], text_align="center", text_baseline="middle")
+
+p.outline_line_color = None
+p.grid.grid_line_color = None
+p.axis.axis_line_color = None
+p.axis.major_tick_line_color = None
+p.axis.major_label_standoff = 0
+p.legend.orientation = "horizontal"
+p.legend.location ="top_center"
+p.hover.renderers = [r] # only hover element boxes
+
+print(source.dataspecs())
+
+# Create a CustomJS callback
+callback = CustomJS(args=dict(source=source), code="""
+ var data = source.data;
+ var selected_elements = [];
+ for (var i = 0; i < data.symbol.length; i++) {
+ if (data.selected[i]) { // Corrected if statement with braces
+ selected_elements.push(data.symbol[i]);
+ }
+ }
+ console.log('Selected elements:', selected_elements);
+ document.dispatchEvent(new CustomEvent("selection_event", {detail: JSON.stringify(selected_elements)}));
+ """)
+ # yield j
+ # st.rerun()
+
+
+
+# Add TapTool with the callback
+tap_tool = TapTool()
+p.add_tools(tap_tool)
+p.js_on_event('tap', callback)
+
+st.bokeh_chart(p, use_container_width=True)
+
+# show(p)
+
+selected_info = st.empty()
+
+# Use session state to store selected elements
+if 'selected_elements' not in st.session_state:
+ st.session_state.selected_elements = []
+
+st.markdown("""
+
+""", unsafe_allow_html=True)
+
+# Display selected elements
+if st.session_state.selected_elements:
+ st.write("Selected Elements:")
+ for element in st.session_state.selected_elements:
+ st.write(f"{element['symbol']} ({element['name']}):")
+ st.write(f" Atomic Number: {element['atomic_number']}")
+ st.write(f" Atomic Mass: {element['atomic_mass']}")
+ st.write(f" Type: {element['metal']}")
+ st.write("---")
+
+else:
+ st.write("No elements selected. Click on elements in the periodic table to select them.")
+ # st.rerun()
+
+# Add a button to clear selection
+if st.button("Clear Selection"):
+ st.session_state.selected_elements = []
+ st.rerun()
diff --git a/tests/test_app.py b/tests/test_app.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8e62c58285f8555a53186be6f8d2a3c7be863cd
--- /dev/null
+++ b/tests/test_app.py
@@ -0,0 +1,27 @@
+import streamlit as st
+from streamlit.testing.v1 import AppTest
+import pytest
+from pathlib import Path
+
+path = Path(__file__).parents[1] / "serve"
+
+@pytest.fixture
+def home():
+ at = AppTest.from_file(str(path / "app.py"), default_timeout=60)
+ at.run()
+ assert not at.exception
+ return at
+
+def test_leaderboard(home):
+ # Test the leaderboard page by simulating navigation.
+ at = home.switch_page(str(path / "leaderboard.py"))
+ assert not at.exception
+
+def test_task_pages(home):
+ # Test each task page using the TASKS registry.
+ from mlip_arena.tasks import REGISTRY as TASKS
+
+ for task, details in TASKS.items():
+ page_path = str(path / f"tasks/{details['task-page']}.py")
+ at = home.switch_page(page_path)
+ assert not at.exception
diff --git a/tests/test_elasticity.py b/tests/test_elasticity.py
new file mode 100644
index 0000000000000000000000000000000000000000..e96d1d0c2e97994d8f2c84645251d3c21fdc33c0
--- /dev/null
+++ b/tests/test_elasticity.py
@@ -0,0 +1,40 @@
+import sys
+
+import numpy as np
+import pytest
+from mlip_arena.models import MLIPEnum
+from mlip_arena.tasks.elasticity import run as ELASTICITY
+from mlip_arena.tasks.utils import get_calculator
+from prefect.testing.utilities import prefect_test_harness
+
+from ase.build import bulk
+
+
+@pytest.mark.skipif(
+ sys.version_info[:2] != (3, 11),
+ reason="avoid prefect race condition on concurrent tasks",
+)
+@pytest.mark.parametrize("model", [MLIPEnum["MACE-MP(M)"]])
+def test_elasticity(model: MLIPEnum):
+ """
+ Test elasticity prefect workflow with a simple cubic lattice.
+ """
+
+ with prefect_test_harness():
+ result = ELASTICITY(
+ atoms=bulk("Cu", "fcc", a=3.6),
+ calculator=get_calculator(
+ calculator_name=model.name,
+ ),
+ optimizer="BFGSLineSearch",
+ optimizer_kwargs=None,
+ filter="FrechetCell",
+ filter_kwargs=None,
+ criterion=None,
+ persist_opt=False,
+ cache_opt=False,
+ )
+ assert isinstance(result, dict)
+ assert isinstance(result["elastic_tensor"], np.ndarray)
+ assert result["elastic_tensor"].shape == (3, 3, 3, 3)
+ assert isinstance(result["elastic_tensor"][0, 0, 0, 0], float)
diff --git a/tests/test_eos.py b/tests/test_eos.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec7c84090abd972ddcaeaa8222921c156ffcda18
--- /dev/null
+++ b/tests/test_eos.py
@@ -0,0 +1,67 @@
+import sys
+
+import pytest
+from ase.build import bulk
+from prefect import flow
+from prefect.testing.utilities import prefect_test_harness
+
+from mlip_arena.models import MLIPEnum
+from mlip_arena.tasks.eos import run as EOS
+from mlip_arena.tasks.utils import get_calculator
+
+
+
+@flow(persist_result=True)
+def single_eos_flow(calculator_name, concurrent=True, cache=False):
+ atoms = bulk("Cu", "fcc", a=3.6)
+
+ return EOS.with_options(
+ refresh_cache=not cache,
+ )(
+ atoms=atoms,
+ calculator=get_calculator(
+ calculator_name=calculator_name,
+ ),
+ optimizer="BFGSLineSearch",
+ optimizer_kwargs=None,
+ filter="FrechetCell",
+ filter_kwargs=None,
+ criterion=dict(
+ fmax=0.1,
+ ),
+ max_abs_strain=0.1,
+ npoints=6,
+ concurrent=concurrent,
+ )
+
+
+@pytest.mark.skipif(
+ sys.version_info[:2] != (3, 11),
+ reason="avoid prefect race condition on concurrent tasks",
+)
+@pytest.mark.parametrize("concurrent", [False])
+@pytest.mark.parametrize("model", [MLIPEnum["MACE-MP(M)"]])
+def test_eos(model: MLIPEnum, concurrent: bool):
+ """
+ Test EOS prefect workflow with a simple cubic lattice.
+ """
+
+ with prefect_test_harness():
+ result = single_eos_flow(
+ calculator_name=model.name,
+ concurrent=concurrent,
+ cache=False,
+ )
+ assert isinstance(b0_scratch := result["b0"], float)
+
+ # @pytest.mark.dependency(depends=["test_eos"])
+ # @pytest.mark.parametrize("model", [MLIPEnum["MACE-MP(M)"]])
+ # def test_eos_cache(model: MLIPEnum):
+
+ result = single_eos_flow(
+ calculator_name=model.name,
+ concurrent=concurrent,
+ cache=True,
+ )
+ assert isinstance(b0_cache := result["b0"], float)
+ assert b0_scratch == pytest.approx(b0_cache, rel=1e-5)
diff --git a/tests/test_external_calculators.py b/tests/test_external_calculators.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9194036ab6f99c68aea8b26dfeea4292ad9687a
--- /dev/null
+++ b/tests/test_external_calculators.py
@@ -0,0 +1,55 @@
+import pytest
+from ase import Atoms
+from ase.calculators.calculator import PropertyNotImplementedError
+import numpy as np
+
+from mlip_arena.models import MLIPEnum
+
+from requests import HTTPError
+from huggingface_hub.errors import LocalTokenNotFoundError
+
+@pytest.mark.parametrize("model", MLIPEnum)
+def test_calculate(model: MLIPEnum):
+
+ if model.name == "ALIGNN":
+ pytest.xfail("ALIGNN has poor file download mechanism")
+
+ if model.name == "ORB":
+ pytest.xfail("Orbital Materials deprecated the model a month after its premature release in favor of ORBv2")
+
+ if model.name == "M3GNet":
+ pytest.xfail("Cache sometimes fails")
+
+ try:
+ calc = MLIPEnum[model.name].value()
+ except (LocalTokenNotFoundError, HTTPError, FileNotFoundError) as e:
+ pytest.skip(str(e))
+
+ atoms = Atoms(
+ "OO",
+ positions=[[0, 0, 0], [1.5, 0, 0]],
+ cell=[10, 10 + 0.001, 10 + 0.002],
+ pbc=True,
+ )
+
+ atoms.calc = calc
+
+ energy = atoms.get_potential_energy()
+
+ assert isinstance(energy, (float, np.float64, np.float32))
+
+ forces = atoms.get_forces()
+ assert isinstance(forces, (np.ndarray, list))
+ assert len(forces) == len(atoms)
+
+ try:
+ stress = atoms.get_stress()
+ except PropertyNotImplementedError:
+ stress = None
+
+ if stress is None:
+ pytest.xfail("Stress calculation is not supported by the model")
+ else:
+ assert isinstance(stress, (np.ndarray, list))
+
+
diff --git a/tests/test_internal_calculators.py b/tests/test_internal_calculators.py
new file mode 100644
index 0000000000000000000000000000000000000000..d09489782f8157101779ed3e1f06b8803db84ce4
--- /dev/null
+++ b/tests/test_internal_calculators.py
@@ -0,0 +1,36 @@
+import numpy as np
+from mlip_arena.models import MLIPCalculator
+from mlip_arena.models.classicals.zbl import ZBL
+
+from ase.build import bulk
+
+
+def test_zbl():
+ calc = MLIPCalculator(model=ZBL(), cutoff=6.0)
+
+ energies = []
+ forces = []
+ stresses = []
+
+ lattice_constants = [1, 3, 5, 7]
+
+ for a in lattice_constants:
+ atoms = bulk("Cu", "fcc", a=a) * (2, 2, 2)
+ atoms.calc = calc
+
+ energies.append(atoms.get_potential_energy())
+ forces.append(atoms.get_forces())
+ stresses.append(atoms.get_stress(voigt=False))
+
+ # test energy monotonicity
+ assert all(np.diff(energies) <= 0), "Energy is not monotonically decreasing with increasing lattice constant"
+
+ # test force vectors are all zeros due to symmetry
+ for f in forces:
+ assert np.allclose(f, 0), "Forces should be zero due to symmetry"
+
+ # test trace of stress is monotonically increasing (less negative) and zero beyond cutoff
+ traces = [np.trace(s) for s in stresses]
+
+ assert all(np.diff(traces) >= 0), "Trace of stress is not monotonically increasing with increasing lattice constant"
+ assert np.allclose(stresses[-1], 0), "Stress should be zero beyond cutoff"
diff --git a/tests/test_md.py b/tests/test_md.py
new file mode 100644
index 0000000000000000000000000000000000000000..a540f37eeeabbb56a2f52c54eedd6d0392a20a8b
--- /dev/null
+++ b/tests/test_md.py
@@ -0,0 +1,29 @@
+
+import sys
+
+import pytest
+from ase.build import bulk
+
+from mlip_arena.models import MLIPEnum
+from mlip_arena.tasks.md import run as MD
+from mlip_arena.tasks.utils import get_calculator
+
+atoms = bulk("Cu", "fcc", a=3.6)
+
+@pytest.mark.skipif(sys.version_info[:2] != (3,11), reason="avoid prefect race condition on concurrent tasks")
+@pytest.mark.parametrize("model", [MLIPEnum["MACE-MP(M)"]])
+def test_nve(model: MLIPEnum):
+
+ result = MD.fn(
+ atoms,
+ calculator=get_calculator(
+ calculator_name=model.name,
+ ),
+ ensemble="nve",
+ dynamics="velocityverlet",
+ total_time=10,
+ time_step=2,
+ dynamics_kwargs={},
+ )
+
+ assert isinstance(result["atoms"].get_potential_energy(), float)
diff --git a/tests/test_mof.py b/tests/test_mof.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5204f2611fbd61bcbc582888e672a9e15f8e508
--- /dev/null
+++ b/tests/test_mof.py
@@ -0,0 +1,43 @@
+import sys
+
+import pytest
+from ase.build import molecule
+from prefect.testing.utilities import prefect_test_harness
+
+from mlip_arena.models import MLIPEnum
+from mlip_arena.tasks.mof.flow import widom_insertion
+from mlip_arena.tasks.mof.input import get_atoms_from_db
+from mlip_arena.tasks.utils import get_calculator
+
+
+@pytest.fixture(autouse=True, scope="session")
+def prefect_test_fixture():
+ with prefect_test_harness():
+ yield
+
+
+@pytest.mark.skipif(
+ sys.version_info[:2] != (3, 11),
+ reason="avoid prefect race condition on concurrent tasks",
+)
+@pytest.mark.parametrize("model", [MLIPEnum["MACE-MP(M)"]])
+def test_widom_insertion(model: MLIPEnum):
+ # with prefect_test_harness():
+ for atoms in get_atoms_from_db("mofs.db"):
+ result = widom_insertion.with_options(
+ refresh_cache=True,
+ )(
+ structure=atoms,
+ gas=molecule("CO2"),
+ calculator=get_calculator(
+ model,
+ dispersion=True,
+ ),
+ num_insertions=10,
+ fold=2,
+ )
+ assert isinstance(result, dict)
+ assert isinstance(result["henry_coefficient"][0], float)
+ assert isinstance(result["averaged_interaction_energy"][0], float)
+ assert isinstance(result["heat_of_adsorption"][0], float)
+ break # only test one MOF
diff --git a/tests/test_neb.py b/tests/test_neb.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f97fbda28a245066d5e73c8e3d63c8bf807a399
--- /dev/null
+++ b/tests/test_neb.py
@@ -0,0 +1,47 @@
+import sys
+
+import pytest
+from mlip_arena.models import MLIPEnum
+from mlip_arena.tasks import NEB_FROM_ENDPOINTS
+from mlip_arena.tasks.utils import get_calculator
+from prefect.testing.utilities import prefect_test_harness
+
+from ase.spacegroup import crystal
+
+pristine = crystal(
+ "Al", [(0, 0, 0)], spacegroup=225, cellpar=[4.05, 4.05, 4.05, 90, 90, 90]
+) * (3, 3, 3)
+
+atoms = pristine.copy()
+del atoms[0]
+start = atoms.copy()
+
+atoms = pristine.copy()
+del atoms[1]
+end = atoms.copy()
+
+
+@pytest.mark.skipif(
+ sys.version_info[:2] != (3, 11),
+ reason="avoid prefect race condition on concurrent tasks",
+)
+@pytest.mark.parametrize("model", [MLIPEnum["MACE-MP(M)"]])
+def test_neb(model: MLIPEnum):
+ """
+ Test NEB prefect workflow with a simple cubic lattice.
+ """
+
+ with prefect_test_harness():
+ result = NEB_FROM_ENDPOINTS(
+ start=start.copy(),
+ end=end.copy(),
+ n_images=5,
+ calculator=get_calculator(
+ calculator_name=model.name,
+ ),
+ optimizer="BFGS",
+ )
+
+ assert isinstance(result, dict)
+ assert isinstance(result["barrier"][0], float)
+ assert isinstance(result["barrier"][1], float)
+ 
+ 
+ 
+ 
+ 
+
+
+