From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields

arXiv:2309.15126v2 Announce Type: replace-cross
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 associated with these representations can …

abstract arxiv cs.lg development dynamics errors fields low machine molecular dynamics physics.chem-ph progress reference reliability simulations test transformer type vast