Systematic assessment of various universal machine‐learning interatomic potentials

Abstract Machine‐learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of ab initio quality over very large time and length scales. More recently, various universal machine‐learning models have be...

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Bibliographic Details

Published in:	Materials Genome Engineering Advances
Main Authors:	Haochen Yu, Matteo Giantomassi, Giuliana Materzanini, Junjie Wang, Gian‐Marco Rignanese
Format:	Article
Language:	English
Published:	Wiley-VCH 2024-09-01
Subjects:	formation energy geometry optimization machine learning phonons universal machine‐learning interatomic potentials verification
Online Access:	https://doi.org/10.1002/mgea.58