  SchNetPack is a toolbox for the development and application of deep neural
networks to the prediction of potential energy surfaces and other
quantum-chemical properties of molecules and materials. It contains basic
building blocks of atomistic neural networks, manages their training and
provides simple access to common benchmark datasets. This allows for an easy
implementation and evaluation of new models. For now, SchNetPack includes
implementations of (weighted) atomcentered symmetry functions and the deep
tensor neural network SchNet as well as ready-to-use scripts that allow to
train these models on molecule and material datasets. Based upon the PyTorch
deep learning framework, SchNetPack allows to efficiently apply the neural
networks to large datasets with millions of reference calculations as well as
parallelize the model across multiple GPUs. Finally, SchNetPack provides an
interface to the Atomic Simulation Environment in order to make trained models
easily accessible to researchers that are not yet familiar with neural
networks.
