Formation of three-dimensional dislocation networks in α-iron twist grain boundaries: Insights from first-principles neural network interatomic potentials

Meng, Fan Shun; Li, Jiu Hui; Shinzato, Shuhei; Matsubara, Kazuki; Geng, Wen Tong; Ogata, Shigenobu

doi:https://doi.org/10.1016/j.commatsci.2025.113812

Formation of three-dimensional dislocation networks in α-iron twist grain boundaries: Insights from first-principles neural network interatomic potentials

Meng, Fan Shun; Li, Jiu Hui; Shinzato, Shuhei et al.

Computational Materials Science, 2025, 253, 113812

Number of Access:226（2025-08-15 01:09 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/101388

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Title	Formation of three-dimensional dislocation networks in α-iron twist grain boundaries: Insights from first-principles neural network interatomic potentials
Creator	CreatorName Meng, Fan Shun
	CreatorName Li, Jiu Hui
	NRID 1000010853202 1000010853202 researchmap permalink 3201 3201 Scopus Author ID 57188863668 57188863668 CreatorName Shinzato, Shuhei
	CreatorName Matsubara, Kazuki
	CreatorName Geng, Wen Tong
	NRID 1000020273584 1000020273584 ORCID 0000-0002-9072-4496 0000-0002-9072-4496 researchmap permalink read0185246 read0185246 Scopus Author ID 7201796489 7201796489 CreatorName Ogata, Shigenobu
Subject	Twist grain boundary
	α-iron
	Atomistic simulation
	Three-dimensional dislocation network
	Density functional theory based neural network interatomic potentials
Description	Meng F.S., Li J.H., Shinzato S., et al. Formation of three-dimensional dislocation networks in α-iron twist grain boundaries: Insights from first-principles neural network interatomic potentials. Computational Materials Science 253, 113812 (2025); https://doi.org/10.1016/j.commatsci.2025.113812.
Abstract	We conducted a systematic analysis of the atomic structure and energy of (001), (110), and (111) twist grain boundaries (TWGBs) in α-iron using a recently developed neural network interatomic potential (NNIP). This study showcases typical dislocation networks within TWGBs that exhibit small twist angles. Notably, we observed a three-dimensional (3D) dislocation network in (111) twist grain boundaries, primarily composed of ½〈111〉 dislocations—structures unattainable using previously proposed empirical potentials, hence unreported in earlier studies. The novel 3D dislocation network was further validated through several approaches, including principal component analysis (PCA), an NNIP ensemble model, and cross-validation with other machine learning interatomic potentials designed for α-iron. This breakthrough offers a new perspective on the properties of twist grain boundaries, potentially impacting our understanding of their strength, toughness, and mobility.
Publisher	Elsevier B.V.
Source Title	Computational Materials Science
Volume (Issue)	253
Page	113812
Date of Issued	2025-03-13
Language	English
Handle URL	https://hdl.handle.net/11094/101388
PISSN	09270256
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1016/j.commatsci.2025.113812
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCMI Type	Text
DCTERMS.bibliographicCitation	Computational Materials Science. 2025, 253, p. 113812
DC.title	Formation of three-dimensional dislocation networks in α-iron twist grain boundaries: Insights from first-principles neural network interatomic potentials
DC.creator	Meng, Fan Shun
	Li, Jiu Hui
	Shinzato, Shuhei
	Matsubara, Kazuki
	Geng, Wen Tong
	Ogata, Shigenobu
DC.publisher	Elsevier B.V.
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-03-13
DC.identifier	https://doi.org/10.1016/j.commatsci.2025.113812
DC.subject	Twist grain boundary
	α-iron
	Atomistic simulation
	Three-dimensional dislocation network
	Density functional theory based neural network interatomic potentials
DC.description	Meng F.S., Li J.H., Shinzato S., et al. Formation of three-dimensional dislocation networks in α-iron twist grain boundaries: Insights from first-principles neural network interatomic potentials. Computational Materials Science 253, 113812 (2025); https://doi.org/10.1016/j.commatsci.2025.113812.
DCTERMS.abstract	We conducted a systematic analysis of the atomic structure and energy of (001), (110), and (111) twist grain boundaries (TWGBs) in α-iron using a recently developed neural network interatomic potential (NNIP). This study showcases typical dislocation networks within TWGBs that exhibit small twist angles. Notably, we observed a three-dimensional (3D) dislocation network in (111) twist grain boundaries, primarily composed of ½〈111〉 dislocations—structures unattainable using previously proposed empirical potentials, hence unreported in earlier studies. The novel 3D dislocation network was further validated through several approaches, including principal component analysis (PCA), an NNIP ensemble model, and cross-validation with other machine learning interatomic potentials designed for α-iron. This breakthrough offers a new perspective on the properties of twist grain boundaries, potentially impacting our understanding of their strength, toughness, and mobility.
DC.rights	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
DC.format	101388
citation_title	Formation of three-dimensional dislocation networks in α-iron twist grain boundaries: Insights from first-principles neural network interatomic potentials
citation_author	Meng, Fan Shun
	Li, Jiu Hui
	Shinzato, Shuhei
	Matsubara, Kazuki
	Geng, Wen Tong
	Ogata, Shigenobu
citation_publisher	Elsevier B.V.
citation_language	English
citation_date	2025-03-13
citation_journal_title	Computational Materials Science
citation_volume	253
citation_firstpage	113812
citation_doi	https://doi.org/10.1016/j.commatsci.2025.113812
citation_public_url	https://hdl.handle.net/11094/101388

Formation of three-dimensional dislocation networks in α-iron twist grain boundaries: Insights from first-principles neural network interatomic potentials

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