18 citations · 38 across the 5 of their papers we have counts for
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physics.chem-ph2023★ 1 cited
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields
J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller +1
Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the re…
cond-mat.mtrl-sci2023★ 18 cited
Stress and heat flux via automatic differentiation
Marcel F. Langer, J. Thorben Frank, Florian Knoop
Machine-learning potentials provide computationally efficient and accurate approximations of the Born-Oppenheimer potential energy surface. This potential determines many materials…