CHGNet-PyG-MatPES-PBE-2025.2.10

Description

This model is a CHGNet universal potential for the PyTorch Geometric (PyG) backend of MatGL. The weights were directly transferred from the DGL checkpoint materialyze/CHGNet-PES-MatPES-PBE-2025.2.10 — no retraining was performed.

The architecture is a faithful PyG port of the original DGL CHGNet implementation. The DGL implementation has a slight modification from the original PyTorch implementation by adding directed edge updates; this PyG port preserves that modification.

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

Identical to the source DGL checkpoint (weights are the same):

PyG vs DGL prediction parity

Evaluated on 10 structures spanning diverse chemistries (MoS, Fe, Mo, Al, NaCl, BaTiO₃, Li₂O, MgO, and their perturbed variants) with both the PyG and DGL models running independently.

| Quantity | Max |ΔPyG − ΔDGL| | |---|---| | Energy/atom (eV) | 0 (exact) | | Forces (eV/Å) | 1.5 × 10⁻⁷ | | Stress (GPa) | 4.1 × 10⁻⁶ | | Magnetic moment (μB) | 0 (exact) |

Non-zero force/stress differences arise from floating-point summation order differences between DGL and PyG message-passing kernels, not from any model divergence. All differences are more than two orders of magnitude below the atol = 1e-5 threshold of the automated parity test.

Usage

import matgl
from matgl.ext.pymatgen import Structure2Graph
import torch

pot = matgl.load_model("BowenD-UCB/CHGNet-PyG-MatPES-PBE-2025.2.10")
pot.eval()

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": "Bowen Deng"
}