SchNetPack 2.0: A neural network toolbox for atomistic machine learning
Kristof T. Schütt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer, Jonas Lederer, Michael Gastegger
Abstract
SchNetPack is a versatile neural network toolbox that addresses both the requirements of method development and the application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks, and 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 a custom code and ready for complex training tasks, such as the generation of 3D molecular structures.
Topics & Concepts
ToolboxComputer scienceArtificial neural networkPipeline (software)Interface (matter)Artificial intelligenceDeep learningComputer architectureMachine learningProgramming languageParallel computingBubbleMaximum bubble pressure methodMachine Learning in Materials ScienceElectronic and Structural Properties of OxidesMass Spectrometry Techniques and Applications