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.

You can find the old SchNetPack 1.0 in the schnetpack1.0 branch

• SchNet - an end-to-end continuous-filter CNN for molecules and materials [1-3]
• PaiNN - equivariant message-passing for molecules and materials [4]
• Output modules for dipole moments, polarizability, stress, and general response properties
• Modules for electrostatics, Ewald summation, ZBL repulsion
• GPU-accelerated molecular dynamics code incl. path-integral MD, thermostats, barostats

• python 3.8
• ASE
• numpy
• PyTorch 1.9
• hydra

Note: We recommend using a GPU for training the neural networks.

pip install schnetpack

git clone https://github.com/atomistic-machine-learning/schnetpack.git
cd schnetpack

pip install -r requirements.txt

pip install .

You're ready to go!

Under construction. For a first test, use:

spktrain experiment=qm9 model/representation=painn

For the full API reference, visit our documentation.

If you are using SchNetPack in your research, please cite:

K.T. Schütt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, K.-R. Müller. SchNetPack: A Deep Learning Toolbox For Atomistic Systems. J. Chem. Theory Comput. 10.1021/acs.jctc.8b00908 arXiv:1809.01072. (2018)

CLI and hydra configs for PyTorch Lightning are adapted from this template:

• [1] K.T. Schütt. F. Arbabzadah. S. Chmiela, K.-R. Müller, A. Tkatchenko.
  Quantum-chemical insights from deep tensor neural networks. Nature Communications 8. 13890 (2017)
  10.1038/ncomms13890

• [2] K.T. Schütt. P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, K.-R. Müller.
  SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. Advances in Neural Information Processing Systems 30, pp. 992-1002 (2017) Paper

• [3] K.T. Schütt. P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, K.-R. Müller.
  SchNet - a deep learning architecture for molecules and materials. The Journal of Chemical Physics 148(24), 241722 (2018) 10.1063/1.5019779

• [4] K. T. Schütt, O. T. Unke, M. Gastegger
  Equivariant message passing for the prediction of tensorial properties and molecular spectra. International Conference on Machine Learning (pp. 9377-9388). PMLR, Paper.