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SchNetPack 2.0: A neural network toolbox for atomistic machine learning.

Authors :
Schütt, Kristof T.
Hessmann, Stefaan S. P.
Gebauer, Niklas W. A.
Lederer, Jonas
Gastegger, Michael
Source :
Journal of Chemical Physics; 4/14/2023, Vol. 158 Issue 14, p1-23, 23p
Publication Year :
2023

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00219606
Volume :
158
Issue :
14
Database :
Complementary Index
Journal :
Journal of Chemical Physics
Publication Type :
Academic Journal
Accession number :
163113548
Full Text :
https://doi.org/10.1063/5.0138367