Machine learning force field for Fe-H system and investigation on role of hydrogen on the crack propagation in α -Fe

Zhang, Buyu; Asta, Mark; Wang, Lin-Wang

doi:10.1016/j.commatsci.2022.111709

Title: Machine learning force field for Fe-H system and investigation on role of hydrogen on the crack propagation in $α$ -Fe

Journal Article · 31 October 2022 · Computational Materials Science

DOI: https://doi.org/10.1016/j.commatsci.2022.111709 · OSTI ID:1882447

; Asta, Mark; Wang, Lin-Wang

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1882447

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Journal Issue: C Vol. 214; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

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Machine learning force field
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Title: Machine learning force field for Fe-H system and investigation on role of hydrogen on the crack propagation in α -Fe

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Title: Machine learning force field for Fe-H system and investigation on role of hydrogen on the crack propagation in $α$ -Fe