---
language:
- en
pretty_name: Materials Potential Energy Surface (MatPES)
tags:
- materials
- science
- pes
- mlip
license: bsd-3-clause
task_categories:
- tabular-regression
configs:
- config_name: pbe
  data_files: MatPES-PBE-2025.1.json.gz
- config_name: r2scan
  data_files: MatPES-R2SCAN-2025.1.json.gz
- config_name: pbe-atoms
  data_files: MatPES-PBE-atoms.json.gz
- config_name: r2scan-atoms
  data_files: MatPES-R2SCAN-atoms.json.gz
size_categories:
- 100K<n<1M
---

## Dataset Description

- **Homepage:** [matpes.ai](http://matpes.ai)
- **Paper:** [A Foundational Potential Energy Surface Dataset for Materials](https://doi.org/10.48550/arXiv.2503.04070)
- **Leaderboard:** [MatCalc-Benchmark](http://matpes.ai/benchmarks)
- **Point of Contact:** [Materials Virtual Lab]

### Dataset Summary

Potential energy surface datasets with near-complete coverage of the periodic table are used to train foundation
potentials (FPs), i.e., machine learning interatomic potentials (MLIPs) with near-complete coverage of the periodic
table.  MatPES is an initiative by the [Materials Virtual Lab] and the [Materials Project] to address
[critical deficiencies](http://matpes.ai/about) in such PES datasets for materials.

1. **Accuracy.** MatPES is computed using static DFT calculations with stringent converegence criteria.
   Please refer to the `MatPESStaticSet` in [pymatgen] for details.
2. **Comprehensiveness.** MatPES structures are sampled using a 2-stage version of DImensionality-Reduced
   Encoded Clusters with sTratified [DIRECT](https//doi.org/10.1038/s41524-024-01227-4) sampling from a greatly expanded configuration of MD structures.
3. **Quality.** MatPES includes computed data from the PBE functional, as well as the high fidelity r2SCAN meta-GGA
   functional with improved description across diverse bonding and chemistries.

The initial v2025.1 release comprises ~400,000 structures from 300K MD simulations. This dataset is much smaller
than other PES datasets in the literature and yet achieves comparable or, in some cases,
[improved performance and reliability](http://matpes.ai/benchmarks) on trained FPs.

MatPES is part of the MatML ecosystem, which includes the [MatGL] (Materials Graph Library) and [maml] (MAterials
Machine Learning) packages, the [MatPES] (Materials Potential Energy Surface) dataset, and the [MatCalc] (Materials
Calculator).


[Materials Virtual Lab]: http://materialsvirtuallab.org
[Materials Project]: https://materialsproject.org
[M3GNet]: http://dx.doi.org/10.1038/s43588-022-00349-3
[CHGNet]: http://doi.org/10.1038/s42256-023-00716-3
[TensorNet]: https://arxiv.org/abs/2306.06482
[maml]: https://materialsvirtuallab.github.io/maml/
[MatGL]: https://matgl.ai
[MatPES]: https://matpes.ai
[MatCalc]: https://matcalc.ai