mavrl/matpes

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 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 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 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).