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Found 4 papers, 3 papers with code
Euclidean Fast Attention: Machine Learning Global Atomic Representations at Linear Cost
Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are often critical for accurate predictions.
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields
Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations.
Stress and heat flux via automatic differentiation
Machine-learning potentials provide computationally efficient and accurate approximations of the Born-Oppenheimer potential energy surface.
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
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.
