A Euclidean transformer for fast and stable machine learned force fields
J. Thorben Frank,
Oliver T. Unke,
Klaus-Robert Müller
et al.
Abstract: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 reliability of MLFFs in molecular dynamics (MD) simulations is facing growing scrutiny due to concerns about instability over extended simulation timescales. Our findings suggest a potential connection between robustness to cumulative inaccuracies and the use of equivariant representations in MLFFs, but the computational cost associate… Show more
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