metadata
library_name: matgl
tags:
- matgl
- materials-science
- graph-neural-network
- machine-learning-interatomic-potential
- foundation-potential
- mlip
Introduction
Pre-trained TensorNet foundation potential, i.e., universal machine learning interatomic potential trained on the MatPES r2SCAN 2025.2 dataset.
Potential
matgl Potential model (version 3).
Usage
import matgl
model = matgl.load_model("materialyze/TensorNet-PES-MatPES-r2SCAN-2025.2")
Stats
- Layers: 2
- Units: 128
- Test_MAE_energies: 32 meV/atom
- Test_MAE_forces: 142 meV/Å
- Test_MAE_stresses: 0.705 GPa
Metadata
{
"dataset": "MatPES-r2SCAN-2025.2"
}