| library_name: matgl | |
| tags: | |
| - matgl | |
| - materials-science | |
| - graph-neural-network | |
| # Description | |
| This model is a M3GNet potential for 4 elements including H, C, N, O. It has broad applications in the | |
| dynamic simulations of organic molecules. | |
| # Training dataset | |
| ANI-1x-Subset: 300K MD simulations and Materials Project ground state calculations. | |
| - Training set size: 991735 | |
| - Validation set size: 248355 | |
| - Test set size: 248355 | |
| # Performance metrics | |
| ## Training and validation errors | |
| MAEs of energies, forces and stresses, respectively | |
| - Training: 2.281 meV/atom, 46 meV/A | |
| - Validation: 2.286 meV/atom, 46 meV/A | |
| - Test: 1.596 meV/atom, 35 meV/A | |
| # References | |
| ```txt | |
| Ko, T.W., Deng, B., Nassar, M. et al. Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. npj Computation Materials 11, 253 (2025). | |
| ``` | |