SchNetPack 2.0: A neural network toolbox for atomistic machine learning

SchNetPack 2.0: A neural network toolbox for atomistic machine learning

11 December 2022

Kristof T. Schütt

Stefaan S. P. Hessmann

Niklas W. A. Gebauer

Papers citing "SchNetPack 2.0: A neural network toolbox for atomistic machine learning"

8 / 8 papers shown

Title
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics Paul Fuchs Stephan Thaler Sebastien Röcken J. Zavadlav DiffM 70 6 0 28 Aug 2024
FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials Thomas Plé Olivier Adjoua Louis Lagardère Jean‐Philip Piquemal 30 8 0 02 May 2024
Multiscale Hodge Scattering Networks for Data Analysis Naoki Saito Stefan C. Schonsheck Eugene Shvarts 32 1 0 17 Nov 2023
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations Oliver T. Unke M. Stohr Stefan Ganscha Thomas Unterthiner Hartmut Maennel ... Daniel Ahlin M. Gastegger L. M. Sandonas A. Tkatchenko Klaus-Robert Muller AI4CE 35 18 0 17 May 2022
Inverse design of 3d molecular structures with conditional generative neural networks Niklas W. A. Gebauer M. Gastegger Stefaan S. P. Hessmann Klaus-Robert Muller Kristof T. Schütt AI4CE 187 166 0 10 Sep 2021
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects Oliver T. Unke Stefan Chmiela M. Gastegger Kristof T. Schütt H. E. Sauceda K. Müller 158 246 0 01 May 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 203 1,238 0 08 Jan 2021
Deep neural network solution of the electronic Schrödinger equation J. Hermann Zeno Schätzle Frank Noé 149 446 0 16 Sep 2019