Convergence acceleration in machine learning potentials for atomistic simulations-Reference-Cited by-同舟云学术

Convergence acceleration in machine learning potentials for atomistic simulations

Published:2022 Issue:1 Volume:1 Page:61-69
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Bayerl Dylan¹,Andolina Christopher M.¹^ORCID,Dwaraknath Shyam²^ORCID,Saidi Wissam A.¹^ORCID

Affiliation:

1. Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15216, USA

2. Energy Technologies Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, USA

Abstract

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.

Funder

National Science Foundation

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2022/DD/D1DD00005E

Reference51 articles.

1. Identification of cathode materials for lithium batteries guided by first-principles calculations

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3. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution

4. Identification of Potential Photovoltaic Absorbers Based on First-Principles Spectroscopic Screening of Materials

5. Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals

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