18 citations · 38 across the 5 of their papers we have counts for
5 papers
Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost
J. Thorben Frank, Stefan Chmiela, Klaus-Robert Müller +1
Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of dist…
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…
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…
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
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-ini…
Learning Neural Network Quantum States with the Linear Method
J. Thorben Frank, Michael J. Kastoryano
Due to the strong correlations present in quantum systems, classical machine learning algorithms like stochastic gradient descent are often insufficient for the training of neural…