2 research outputs found
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
SchNetPack is a versatile neural networks toolbox that addresses both the
requirements of method development and application of atomistic machine
learning. Version 2.0 comes with an improved data pipeline, modules for
equivariant neural networks as well as a PyTorch implementation of molecular
dynamics. An optional integration with PyTorch Lightning and the Hydra
configuration framework powers a flexible command-line interface. This makes
SchNetPack 2.0 easily extendable with custom code and ready for complex
training task such as generation of 3d molecular structures