Efficient interatomic descriptors for accurate machine learning force fields of extended molecules

Abstract Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF sim...

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Bibliographic Details

Published in:	Nature Communications
Main Authors:	Adil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, Igor Poltavsky, Alexandre Tkatchenko
Format:	Article
Language:	English
Published:	Nature Portfolio 2023-06-01
Online Access:	https://doi.org/10.1038/s41467-023-39214-w