2 code implementations • 11 Dec 2022 • Kristof T. Schütt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer, Jonas Lederer, Michael Gastegger
SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning.
1 code implementation • 10 Sep 2021 • Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, Klaus-Robert Müller, Kristof T. Schütt
The rational design of molecules with desired properties is a long-standing challenge in chemistry.
1 code implementation • NeurIPS 2019 • Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt
Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials.
no code implementations • 26 Oct 2018 • Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt
Discovery of atomistic systems with desirable properties is a major challenge in chemistry and material science.
