A Neural Network Interatomic Potential for the Ternary α-Fe-C-H System: Toward Million-Atom Simulations of Hydrogen Embrittlement in Steel

Meng, Fan Shun; Shinzato, Shuhei; Matsubara, Kazuki; Du, Jun Ping; Yu, Peijun; Ogata, Shigenobu

doi:https://doi.org/10.1007/s11837-025-07721-4

A Neural Network Interatomic Potential for the Ternary α-Fe-C-H System: Toward Million-Atom Simulations of Hydrogen Embrittlement in Steel

Meng, Fan Shun; Shinzato, Shuhei; Matsubara, Kazuki et al.

JOM, 2025, 77(11), 8101-8117

アクセス数:198件（2026-02-16 10:17 集計）

固定URL: https://hdl.handle.net/11094/103272

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タイトル	A Neural Network Interatomic Potential for the Ternary α-Fe-C-H System: Toward Million-Atom Simulations of Hydrogen Embrittlement in Steel
著者	ORCID 0000-0002-2009-1558 0000-0002-2009-1558 researchmap permalink mengfs-ou mengfs-ou Scopus Author ID 55786428200 55786428200 著者名 Meng, Fan Shun
	NRID 1000010853202 1000010853202 researchmap permalink 3201 3201 Scopus Author ID 57188863668 57188863668 著者名 Shinzato, Shuhei
	著者名 Matsubara, Kazuki
	著者名 Du, Jun Ping
	著者名 Yu, Peijun
	NRID 1000020273584 1000020273584 ORCID 0000-0002-9072-4496 0000-0002-9072-4496 researchmap permalink read0185246 read0185246 Scopus Author ID 7201796489 7201796489 著者名 Ogata, Shigenobu
内容	The version of record of this article, first published in JOM 77 (2025), is available online at Publisher’s website: https://doi.org/10.1007/s11837-025-07721-4
抄録	A neural network interatomic potential (NNIP) has been developed for the ternary system of α-iron, carbon, and hydrogen to clarify the degradation behavior of Fe-C steels in hydrogen-rich environments. The NNIP was trained on an extensive reference database generated from spin-polarized density functional theory (DFT) calculations. It demonstrates remarkable performance in various scenarios relevant to Fe and Fe-C systems under hydrogen, including the diffusion kinetics of H and C in Fe and their thermodynamic interactions with iron vacancies, grain boundaries, screw dislocations, cementite, and cementite–ferrite interfaces. Using this NNIP, we conducted large-scale (one-million-atom) molecular dynamics (MD) simulations of uniaxial tensile tests on C-containing α-Fe both with and without H, showing that hydrogen enhances defect accumulation during plastic deformation, which may eventually lead to material failure.
出版者	Springer
収録物名	JOM
巻(号)	77(11)
ページ	8101-8117
刊行年月	2025-10-06
言語	英語
ハンドルURL	https://hdl.handle.net/11094/103272
PISSN	10474838
eISSN	15431851
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1007/s11837-025-07721-4
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	JOM. 2025, 77(11), p. 8101-8117
DC.title	A Neural Network Interatomic Potential for the Ternary α-Fe-C-H System: Toward Million-Atom Simulations of Hydrogen Embrittlement in Steel
DC.creator	Meng, Fan Shun
	Shinzato, Shuhei
	Matsubara, Kazuki
	Du, Jun Ping
	Yu, Peijun
	Ogata, Shigenobu
DC.publisher	Springer
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-10-06
DC.identifier	https://doi.org/10.1007/s11837-025-07721-4
DC.description	The version of record of this article, first published in JOM 77 (2025), is available online at Publisher’s website: https://doi.org/10.1007/s11837-025-07721-4
DCTERMS.abstract	A neural network interatomic potential (NNIP) has been developed for the ternary system of α-iron, carbon, and hydrogen to clarify the degradation behavior of Fe-C steels in hydrogen-rich environments. The NNIP was trained on an extensive reference database generated from spin-polarized density functional theory (DFT) calculations. It demonstrates remarkable performance in various scenarios relevant to Fe and Fe-C systems under hydrogen, including the diffusion kinetics of H and C in Fe and their thermodynamic interactions with iron vacancies, grain boundaries, screw dislocations, cementite, and cementite–ferrite interfaces. Using this NNIP, we conducted large-scale (one-million-atom) molecular dynamics (MD) simulations of uniaxial tensile tests on C-containing α-Fe both with and without H, showing that hydrogen enhances defect accumulation during plastic deformation, which may eventually lead to material failure.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	103272
citation_title	A Neural Network Interatomic Potential for the Ternary α-Fe-C-H System: Toward Million-Atom Simulations of Hydrogen Embrittlement in Steel
citation_author	Meng, Fan Shun
	Shinzato, Shuhei
	Matsubara, Kazuki
	Du, Jun Ping
	Yu, Peijun
	Ogata, Shigenobu
citation_publisher	Springer
citation_language	英語
citation_date	2025-10-06
citation_journal_title	JOM
citation_volume	77
citation_issue	11
citation_firstpage	8101
citation_lastpage	8117
citation_doi	https://doi.org/10.1007/s11837-025-07721-4
citation_public_url	https://hdl.handle.net/11094/103272

A Neural Network Interatomic Potential for the Ternary α-Fe-C-H System: Toward Million-Atom Simulations of Hydrogen Embrittlement in Steel

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