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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](https://github.com/materialsvirtuallab/matgl) `Potential` model (version 3).
## Usage
```python
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
```json
{
"dataset": "MatPES-r2SCAN-2025.2"
}
```
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