Convergence acceleration in machine learning potentials for atomistic simulations

Bayerl, Dylan; Andolina, Christopher M.; Dwaraknath, Shyam; Saidi, Wissam A.

doi:10.1039/D1DD00005E

Title: Convergence acceleration in machine learning potentials for atomistic simulations

Journal Article · 13 February 2022 · Digital Discovery

DOI: https://doi.org/10.1039/D1DD00005E · OSTI ID:1841053

Bayerl, Dylan ^[1];

^[1];

^[2];

^[1]

Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15216, USA
Energy Technologies Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, USA

Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory simulations without appreciably sacrificing accuracy of material property prediction.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0012704; AC02-05CH11231; AC02-06CH11357

OSTI ID:: 1841053

Journal Information:: Digital Discovery, Journal Name: Digital Discovery Journal Issue: 1 Vol. 1; ISSN DDIIAI; ISSN 2635-098X

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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