MatPES

Development of robust, high fidelity datasets for training universal machine learning interatomic potentials

Overview

The materials potential energy surface (MatPES) collaboration aims to generate low noise, high coverage small datasets of DFT-computed properties (energies, forces, stresses, magnetic moments, etc.) for training universal machine learning interatomic potentials.

The data is generated with the MatPESStaticSet in pymatgen, with efficient PBE and r²SCAN workflows implemented in atomate2.

The dataset can be bulk downloaded from MPContribs and uses the ['MatPESTrainDoc` schema from emmet-core](https://github.com/materialsproject/emmet/blob/56840ac7110096636565809cd72036fbb064392e/emmet-core/emmet/core/ml.py#L393)

For analysis tools, see matpes.ai

Citation:

[1] A. D. Kaplan, R. Liu, J. Qi, T. W. Ko, B. Deng, J. Riebesell, G. Ceder, K. A. Persson, and S. P. Ong, “A foundational potential energy surface dataset for materials,” arXiv:2503.04070, yr. 2025, DOI: https://doi.org/10.48550/arXiv.2503.04070