| library_name: matgl | |
| tags: | |
| - matgl | |
| - materials-science | |
| - graph-neural-network | |
| - machine-learning-interatomic-potential | |
| - foundation-potential | |
| - mlip | |
| # TensorNet-PES-MatPES-r2SCAN-2025.2-m | |
| ## Introduction | |
| Pre-trained TensorNet foundation potential, i.e., universal machine learning interatomic potential trained on the MatPES-r2SCAN-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-r2SCAN-2025.2` reference (0.84M). | |
| ## Potential | |
| [matgl](https://github.com/materialyzeai/matgl) `Potential` model (version 3). | |
| ## Usage | |
| ```python | |
| import matgl | |
| model = matgl.load_model("materialyze/TensorNet-PES-MatPES-r2SCAN-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 | 0.034200 | 0.125625 | 0.451469 | | |
| | Validation | 0.035304 | 0.155005 | 0.658389 | | |
| | Test | 0.035895 | 0.154268 | 0.667323 | | |
| ## Metadata | |
| ```json | |
| { | |
| "dataset": "MatPES-r2SCAN-2025.2", | |
| } | |
| ``` | |