1 code implementation • 21 Sep 2023 • J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller, Stefan Chmiela
Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations.
3 code implementations • 2 May 2023 • Marcel F. Langer, J. Thorben Frank, Florian Knoop
Machine-learning potentials provide computationally efficient and accurate approximations of the Born-Oppenheimer potential energy surface.
1 code implementation • 28 May 2022 • J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller
The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods.
