Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic

Correction to "Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic". J. Chem. Theory Comput. 2025, 21, 17, 8601–8613. DOI https://doi.org/10.1021/acs.jctc.5c00791

Detalles Bibliográficos
Autores: Yang, Yongpeng, Han, Jingli, Viñes Solana, Francesc, Illas i Riera, Francesc
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/228056
Acceso en línea:https://hdl.handle.net/2445/228056
Access Level:acceso abierto
Palabra clave:Nanoestructures
Nanoquímica
Nanostructures
Nanochemistry
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spelling Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered CubicYang, YongpengHan, JingliViñes Solana, FrancescIllas i Riera, FrancescNanoestructuresNanoquímicaNanostructuresNanochemistryCorrection to "Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic". J. Chem. Theory Comput. 2025, 21, 17, 8601–8613. DOI https://doi.org/10.1021/acs.jctc.5c00791A highly accurate high-dimensional neural network potential (HDNNP), trained using more than 180,000 DFT-calculated structures, is used to investigate the structure or realistic Cu−Ag bimetallic particles, as this is the dominant species during the CO2 reduction process. The structural transition of Cu and Ag nanoparticles of increasing size, ranging from hundreds of atoms to tens of thousands of atoms, has been studied. Global optimization shows that all Cu and Ag nanoparticles containing 100 to 1000 atoms have an icosahedral core. Upon increasing the number of atoms to 6000 and 10,000 for Cu and Ag, respectively, the nanoparticles’ structural transitions from icosahedral to truncated-octahedral. For even larger nanoparticles, the (100)/(111) surface ratio in truncated-octahedral structures increases, which finally leads to a transformation into the cuboctahedral shape as observed in experiments.American Chemical Society2026202620252026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion13 p.application/pdfhttps://hdl.handle.net/2445/228056Articles publicats en revistes (Ciència dels Materials i Química Física)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1021/acs.jctc.5c00791Journal of Chemical Theory and Computation, 2025, vol. 21, num.17, p. 8601-8613https://doi.org/10.1021/acs.jctc.5c00791cc by (c) Yang, Yongpeng et al., 2025https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2280562026-05-29T05:05:01Z
dc.title.none.fl_str_mv Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic
title Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic
spellingShingle Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic
Yang, Yongpeng
Nanoestructures
Nanoquímica
Nanostructures
Nanochemistry
title_short Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic
title_full Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic
title_fullStr Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic
title_full_unstemmed Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic
title_sort Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic
dc.creator.none.fl_str_mv Yang, Yongpeng
Han, Jingli
Viñes Solana, Francesc
Illas i Riera, Francesc
author Yang, Yongpeng
author_facet Yang, Yongpeng
Han, Jingli
Viñes Solana, Francesc
Illas i Riera, Francesc
author_role author
author2 Han, Jingli
Viñes Solana, Francesc
Illas i Riera, Francesc
author2_role author
author
author
dc.subject.none.fl_str_mv Nanoestructures
Nanoquímica
Nanostructures
Nanochemistry
topic Nanoestructures
Nanoquímica
Nanostructures
Nanochemistry
description Correction to "Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic". J. Chem. Theory Comput. 2025, 21, 17, 8601–8613. DOI https://doi.org/10.1021/acs.jctc.5c00791
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/228056
url https://hdl.handle.net/2445/228056
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1021/acs.jctc.5c00791
Journal of Chemical Theory and Computation, 2025, vol. 21, num.17, p. 8601-8613
https://doi.org/10.1021/acs.jctc.5c00791
dc.rights.none.fl_str_mv cc by (c) Yang, Yongpeng et al., 2025
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc by (c) Yang, Yongpeng et al., 2025
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13 p.
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv Articles publicats en revistes (Ciència dels Materials i Química Física)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869420148077625344
score 15,198674

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