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--- |
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language: |
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- en |
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pretty_name: Materials Potential Energy Surface (MatPES) |
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tags: |
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- materials |
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- science |
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- pes |
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- mlip |
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license: bsd-3-clause |
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task_categories: |
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- tabular-regression |
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configs: |
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- config_name: pbe |
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data_files: MatPES-PBE-2025.1.json.gz |
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- config_name: r2scan |
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data_files: MatPES-R2SCAN-2025.1.json.gz |
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- config_name: pbe-atoms |
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data_files: MatPES-PBE-atoms.json.gz |
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- config_name: r2scan-atoms |
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data_files: MatPES-R2SCAN-atoms.json.gz |
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size_categories: |
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- 100K<n<1M |
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--- |
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## Dataset Description |
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- **Homepage:** [matpes.ai](http://matpes.ai) |
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- **Paper:** [A Foundational Potential Energy Surface Dataset for Materials](https://doi.org/10.48550/arXiv.2503.04070) |
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- **Leaderboard:** [MatCalc-Benchmark](http://matpes.ai/benchmarks) |
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- **Point of Contact:** [Materials Virtual Lab] |
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### Dataset Summary |
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Potential energy surface datasets with near-complete coverage of the periodic table are used to train foundation |
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potentials (FPs), i.e., machine learning interatomic potentials (MLIPs) with near-complete coverage of the periodic |
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table. MatPES is an initiative by the [Materials Virtual Lab] and the [Materials Project] to address |
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[critical deficiencies](http://matpes.ai/about) in such PES datasets for materials. |
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1. **Accuracy.** MatPES is computed using static DFT calculations with stringent converegence criteria. |
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Please refer to the `MatPESStaticSet` in [pymatgen] for details. |
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2. **Comprehensiveness.** MatPES structures are sampled using a 2-stage version of DImensionality-Reduced |
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Encoded Clusters with sTratified [DIRECT](https//doi.org/10.1038/s41524-024-01227-4) sampling from a greatly expanded configuration of MD structures. |
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3. **Quality.** MatPES includes computed data from the PBE functional, as well as the high fidelity r2SCAN meta-GGA |
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functional with improved description across diverse bonding and chemistries. |
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The initial v2025.1 release comprises ~400,000 structures from 300K MD simulations. This dataset is much smaller |
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than other PES datasets in the literature and yet achieves comparable or, in some cases, |
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[improved performance and reliability](http://matpes.ai/benchmarks) on trained FPs. |
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MatPES is part of the MatML ecosystem, which includes the [MatGL] (Materials Graph Library) and [maml] (MAterials |
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Machine Learning) packages, the [MatPES] (Materials Potential Energy Surface) dataset, and the [MatCalc] (Materials |
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Calculator). |
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[Materials Virtual Lab]: http://materialsvirtuallab.org |
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[Materials Project]: https://materialsproject.org |
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[M3GNet]: http://dx.doi.org/10.1038/s43588-022-00349-3 |
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[CHGNet]: http://doi.org/10.1038/s42256-023-00716-3 |
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[TensorNet]: https://arxiv.org/abs/2306.06482 |
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[maml]: https://materialsvirtuallab.github.io/maml/ |
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[MatGL]: https://matgl.ai |
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[MatPES]: https://matpes.ai |
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[MatCalc]: https://matcalc.ai |
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