materialyze/CHGNet-PES-MatPES-PBE-2025.2.10

Description

This model is a CHGNet universal potential trained from the Materials Potential Energy Surface (MatPES) dataset that contains over 1.5 million structures with 89 elements. This Matgl implementation has slight modification from original pytorch implementation by adding directed edge updates.

Training dataset

MatPES-PBE-2024.11: Materials Energy Surface dataset that contains off-equilibrium PBE static calculations.

• Train-Val-Test splitting with mp-id: 0.9 - 0.5 - 0.5
• Train set size: 391241
• Validation set size: 21736
• Test set size: 21735

Performance metrics

Training and validation errors

partition	Energy (meV/atom)	Force (meV/A)	stress (GPa)	magmom (muB)
Train	26.54	81.4	0.375	0.066
Validation	32.02	123.8	0.617	0.067
Test	30.70	136.0	0.642	0.066

References

Deng, B. et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling.
Nat. Mach. Intell. 1–11 (2023) doi:10.1038/s42256-023-00716-3.

Date: 2025.2.10

Author: Bowen Deng

Metadata

{
  "tags": [
    "matgl",
    "materials-science",
    "graph-neural-network"
  ],
  "license": "BSD-3-Clause",
  "author": "Materialyze"
}