16 dependents
| Package | Description | Downloads/month |
|---|---|---|
| Atomistic machine learning models you can use everywhere for everything | 40K | |
| SchNetPack - Deep Neural Networks for Atomistic Systems | 33K | |
| Atomistic machine learning models you can use everywhere for everything | 11K | |
| Train, fine-tune, and manipulate machine learning models for atomistic systems | 10K | |
| Display and Edit Molecules (https://zndraw.icp.uni-stuttgart.de) | 7K | |
| Extension functionalities for ASE (Atomic Simulation Environment) | 5K | |
| train and use graph-based ML models of potential energy surfaces | 3K | |
| TorchSim integration for metatomic models | 2K | |
| A flexible and performant framework for training machine learning potentials. | 973 | |
| experimental tooling for training machine learning interatomic potentials in jax | 764 | |
| Molecular dynamics post-processing toolbox | 533 | |
| Topological analysis tools for network materials. | 521 | |
| Molecular dynamics post-processing and analysis package | 285 | |
| The Chemical Core Class for Graph Theory Analysis & Graph Neural Network. | 282 | |
| Predictions of chemical shieldings using machine learning | 259 | |
| Add your description here | 213 |
