Metadata-Version: 2.1
Name: schnetpack
Version: 1.0.1
Summary: SchNetPack - Deep Neural Networks for Atomistic Systems
Home-page: https://github.com/atomistic-machine-learning/schnetpack
Author: Kristof T. Schuett, Michael Gastegger, Pan Kessel, Kim Nicoli
License: MIT
Requires-Python: >=3.6
License-File: LICENSE
Requires-Dist: torch (>=1.8.0)
Requires-Dist: numpy
Requires-Dist: ase (>=3.21)
Requires-Dist: h5py
Requires-Dist: tensorboardX
Requires-Dist: tqdm
Requires-Dist: pyyaml
Provides-Extra: test
Requires-Dist: pytest ; extra == 'test'
Requires-Dist: pytest-console-scripts ; extra == 'test'
Requires-Dist: pytest-datadir ; extra == 'test'


        SchNetPack aims to provide accessible atomistic neural networks that can be
        trained and applied out-of-the-box, while still being extensible to custom 
        atomistic architectures
