Machine-learned search for the stable structures of silicene on Ag(111)

Hamamoto, Yuji; Pham, Thanh Ngoc; Bisbo, Malthe K.; Hammer, Bjørk; Morikawa, Yoshitada

doi:https://doi.org/10.1103/PhysRevMaterials.7.124002

Machine-learned search for the stable structures of silicene on Ag(111)

Hamamoto, Yuji; Pham, Thanh Ngoc; Bisbo, Malthe K. et al.

Physical Review Materials, 2023, 7(12), 124002

Number of Access:599（2025-10-09 07:20 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/93530

Link to primary information

File	Format	Terms of use	Size	Views	Date.Available	Description	information
PhysicRevMat_7_12_124002	pdf	None	5.62 MB	106	2024.02.01

Creative Commons : Attribution

Item Information

Output File Export EndNote Basic Export Mendeley

Title	Machine-learned search for the stable structures of silicene on Ag(111)
Creator	NRID 1000030584734 1000030584734 ORCID 0000-0003-4377-7015 0000-0003-4377-7015 researchmap permalink yuji_hamamoto yuji_hamamoto Scopus Author ID 23990614100 23990614100 CreatorName Hamamoto, Yuji
	Scopus Author ID 57202194674 57202194674 ORCID 0000-0001-5141-8720 0000-0001-5141-8720 CreatorName Pham, Thanh Ngoc
	CreatorName Bisbo, Malthe K.
	CreatorName Hammer, Bjørk
	NRID 1000080358184 1000080358184 researchmap permalink read0118585 read0118585 Scopus Author ID 56042230400 56042230400 CreatorName Morikawa, Yoshitada
Description	Hamamoto Y., Pham T.N., Bisbo M.K., et al. Machine-learned search for the stable structures of silicene on Ag(111). Physical Review Materials 7, 124002 (2023); https://doi.org/10.1103/PhysRevMaterials.7.124002.
Abstract	The honeycomb lattice of silicene exhibits a variety of nontrivial reconstructions on the Ag(111) surface, whose diversity hampers the theoretical prediction of experimentally unidentified structures using computationally expensive density functional theory (DFT) calculations. We here apply an efficient method based on an evolutionary algorithm and a Gaussian process, which is trained on the fly with DFT calculations, to the search for the stable structures of silicene on Ag(111). We demonstrate that the structure search method can not only reproduce the well-known structures, but also predict the existence of metastable structures that are close in stability to the most stable ones. Detailed analyses of the obtained results reveal that such metastable structures play crucial roles in the stabilization of less ordered phases often observed experimentally. The present method can replace the conventional manual search based on intuition and is widely applicable to the investigations of new systems such as emerging two-dimensional materials.
Publisher	American Physical Society
Source Title	Physical Review Materials
Volume (Issue)	7(12)
Page	124002
Date of Issued	2023-12-21
Language	English
Handle URL	https://hdl.handle.net/11094/93530
eISSN	24759953
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1103/PhysRevMaterials.7.124002
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 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	Physical Review Materials. 2023, 7(12), p. 124002
DC.title	Machine-learned search for the stable structures of silicene on Ag(111)
DC.creator	Hamamoto, Yuji
	Pham, Thanh Ngoc
	Bisbo, Malthe K.
	Hammer, Bjørk
	Morikawa, Yoshitada
DC.publisher	American Physical Society
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2023-12-21
DC.identifier	https://doi.org/10.1103/PhysRevMaterials.7.124002
DC.description	Hamamoto Y., Pham T.N., Bisbo M.K., et al. Machine-learned search for the stable structures of silicene on Ag(111). Physical Review Materials 7, 124002 (2023); https://doi.org/10.1103/PhysRevMaterials.7.124002.
DCTERMS.abstract	The honeycomb lattice of silicene exhibits a variety of nontrivial reconstructions on the Ag(111) surface, whose diversity hampers the theoretical prediction of experimentally unidentified structures using computationally expensive density functional theory (DFT) calculations. We here apply an efficient method based on an evolutionary algorithm and a Gaussian process, which is trained on the fly with DFT calculations, to the search for the stable structures of silicene on Ag(111). We demonstrate that the structure search method can not only reproduce the well-known structures, but also predict the existence of metastable structures that are close in stability to the most stable ones. Detailed analyses of the obtained results reveal that such metastable structures play crucial roles in the stabilization of less ordered phases often observed experimentally. The present method can replace the conventional manual search based on intuition and is widely applicable to the investigations of new systems such as emerging two-dimensional materials.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	93530
citation_title	Machine-learned search for the stable structures of silicene on Ag(111)
citation_author	Hamamoto, Yuji
	Pham, Thanh Ngoc
	Bisbo, Malthe K.
	Hammer, Bjørk
	Morikawa, Yoshitada
citation_publisher	American Physical Society
citation_language	English
citation_date	2023-12-21
citation_journal_title	Physical Review Materials
citation_volume	7
citation_issue	12
citation_firstpage	124002
citation_doi	https://doi.org/10.1103/PhysRevMaterials.7.124002
citation_public_url	https://hdl.handle.net/11094/93530

Machine-learned search for the stable structures of silicene on Ag(111)

Link to primary information

Item Information

Show details