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BowenD-UCB/
CHGNet-PyG-MatPES-r2SCAN-2025.2.10

Name: CHGNet-PyG-MatPES-r2SCAN-2025.2.10
Availability: InStock

matgl

materials-science

graph-neural-network

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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-r2SCAN-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-r2SCAN-2024.11: Materials Energy Surface dataset that contains off-equilibrium r2SCAN static calculations.

Train-Val-Test splitting with mp-id: 0.9 - 0.5 - 0.5
Train set size: 349107
Validation set size: 19395
Test set size: 19395

Performance metrics

Training and validation errors

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

partition	Energy (meV/atom)	Force (meV/Å)	stress (GPa)	magmom (μB)
Train	26.36	85.7	0.359	0.067
Validation	27.51	150.5	0.705	0.066
Test	30.45	156.5	0.735	0.072

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) | 2.7 × 10⁻⁷ | | Stress (GPa) | 4.2 × 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

python

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

pot = matgl.load_model("BowenD-UCB/CHGNet-PyG-MatPES-r2SCAN-2025.2.10")
pot.eval()

References

txt

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

json

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