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
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