metadata
library_name: matgl
tags:
- matgl
- materials-science
- graph-neural-network
- machine-learning-interatomic-potential
- foundation-potential
- mlip
TensorNet-PES-MatPES-PBE-2025.2-m
Introduction
Pre-trained TensorNet foundation potential, i.e., universal machine learning interatomic potential trained on the MatPES-PBE-2025.2 dataset. This is a medium-size TensorNet variant (~1.07M parameters; units=128, nblocks=3), one block deeper than the standard materialyze/TensorNet-PES-MatPES-PBE-2025.2 reference (0.84M).
Potential
matgl Potential model (version 3).
Usage
import matgl
model = matgl.load_model("materialyze/TensorNet-PES-MatPES-PBE-2025.2-m")
Model Details
- Number of parameters: 1,067,906
Metrics
| Split | Energy MAE (eV/atom) | Force MAE (eV/A) | Stress MAE (GPa) |
|---|---|---|---|
| Train | 0.037036 | 0.111566 | 0.440905 |
| Validation | 0.037056 | 0.130899 | 0.592677 |
| Test | 0.037167 | 0.125499 | 0.592749 |
Metadata
{
"dataset": "MatPES-PBE-2025.2",
}