Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations

Autores
Pascuet, Maria Ines Magdalena; Castin, N.; Becquart, C.S.; Malerba, L.
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
An atomistic kinetic Monte Carlo (AKMC) method has been applied to study the stability and mobility of copper-vacancy clusters in Fe. This information, which cannot be obtained directly from experimental measurements, is needed to parameterise models describing the nanostructure evolution under irradiation of Fe alloys (e.g. model alloys for reactor pressure vessel steels). The physical reliability of the AKMC method has been improved by employing artificial intelligence techniques for the regression of the activation energies required by the model as input. These energies are calculated allowing for the effects of local chemistry and relaxation, using an interatomic potential fitted to reproduce them as accurately as possible and the nudged-elastic-band method. The model validation was based on comparison with available ab initio calculations for verification of the used cohesive model, as well as with other models and theories.
Fil: Pascuet, Maria Ines Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Castin, N.. Université Libre de Bruxelles; Bélgica
Fil: Becquart, C.S.. Université de Lille; Francia
Fil: Malerba, L.. No especifíca;
Materia
Cuvacancy clusters
FeCu alloys
Atomistic kinetic Monte Carlo
Artificial neural networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/192059
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculationsPascuet, Maria Ines MagdalenaCastin, N.Becquart, C.S.Malerba, L.Cuvacancy clustersFeCu alloysAtomistic kinetic Monte CarloArtificial neural networkshttps://purl.org/becyt/ford/2.5https://purl.org/becyt/ford/2An atomistic kinetic Monte Carlo (AKMC) method has been applied to study the stability and mobility of copper-vacancy clusters in Fe. This information, which cannot be obtained directly from experimental measurements, is needed to parameterise models describing the nanostructure evolution under irradiation of Fe alloys (e.g. model alloys for reactor pressure vessel steels). The physical reliability of the AKMC method has been improved by employing artificial intelligence techniques for the regression of the activation energies required by the model as input. These energies are calculated allowing for the effects of local chemistry and relaxation, using an interatomic potential fitted to reproduce them as accurately as possible and the nudged-elastic-band method. The model validation was based on comparison with available ab initio calculations for verification of the used cohesive model, as well as with other models and theories.Fil: Pascuet, Maria Ines Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Castin, N.. Université Libre de Bruxelles; BélgicaFil: Becquart, C.S.. Université de Lille; FranciaFil: Malerba, L.. No especifíca;Elsevier Science2011-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/192059Pascuet, Maria Ines Magdalena; Castin, N.; Becquart, C.S.; Malerba, L.; Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations; Elsevier Science; Journal of Nuclear Materials; 412; 1; 5-2011; 106-1150022-3115CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0022311511002352info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jnucmat.2011.02.038info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:08:11Zoai:ri.conicet.gov.ar:11336/192059instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:08:11.94CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations
title Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations
spellingShingle Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations
Pascuet, Maria Ines Magdalena
Cuvacancy clusters
FeCu alloys
Atomistic kinetic Monte Carlo
Artificial neural networks
title_short Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations
title_full Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations
title_fullStr Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations
title_full_unstemmed Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations
title_sort Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations
dc.creator.none.fl_str_mv Pascuet, Maria Ines Magdalena
Castin, N.
Becquart, C.S.
Malerba, L.
author Pascuet, Maria Ines Magdalena
author_facet Pascuet, Maria Ines Magdalena
Castin, N.
Becquart, C.S.
Malerba, L.
author_role author
author2 Castin, N.
Becquart, C.S.
Malerba, L.
author2_role author
author
author
dc.subject.none.fl_str_mv Cuvacancy clusters
FeCu alloys
Atomistic kinetic Monte Carlo
Artificial neural networks
topic Cuvacancy clusters
FeCu alloys
Atomistic kinetic Monte Carlo
Artificial neural networks
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.5
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv An atomistic kinetic Monte Carlo (AKMC) method has been applied to study the stability and mobility of copper-vacancy clusters in Fe. This information, which cannot be obtained directly from experimental measurements, is needed to parameterise models describing the nanostructure evolution under irradiation of Fe alloys (e.g. model alloys for reactor pressure vessel steels). The physical reliability of the AKMC method has been improved by employing artificial intelligence techniques for the regression of the activation energies required by the model as input. These energies are calculated allowing for the effects of local chemistry and relaxation, using an interatomic potential fitted to reproduce them as accurately as possible and the nudged-elastic-band method. The model validation was based on comparison with available ab initio calculations for verification of the used cohesive model, as well as with other models and theories.
Fil: Pascuet, Maria Ines Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Castin, N.. Université Libre de Bruxelles; Bélgica
Fil: Becquart, C.S.. Université de Lille; Francia
Fil: Malerba, L.. No especifíca;
description An atomistic kinetic Monte Carlo (AKMC) method has been applied to study the stability and mobility of copper-vacancy clusters in Fe. This information, which cannot be obtained directly from experimental measurements, is needed to parameterise models describing the nanostructure evolution under irradiation of Fe alloys (e.g. model alloys for reactor pressure vessel steels). The physical reliability of the AKMC method has been improved by employing artificial intelligence techniques for the regression of the activation energies required by the model as input. These energies are calculated allowing for the effects of local chemistry and relaxation, using an interatomic potential fitted to reproduce them as accurately as possible and the nudged-elastic-band method. The model validation was based on comparison with available ab initio calculations for verification of the used cohesive model, as well as with other models and theories.
publishDate 2011
dc.date.none.fl_str_mv 2011-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/192059
Pascuet, Maria Ines Magdalena; Castin, N.; Becquart, C.S.; Malerba, L.; Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations; Elsevier Science; Journal of Nuclear Materials; 412; 1; 5-2011; 106-115
0022-3115
CONICET Digital
CONICET
url http://hdl.handle.net/11336/192059
identifier_str_mv Pascuet, Maria Ines Magdalena; Castin, N.; Becquart, C.S.; Malerba, L.; Stability and mobility of Cu-vacancy clusters in Fe-Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations; Elsevier Science; Journal of Nuclear Materials; 412; 1; 5-2011; 106-115
0022-3115
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0022311511002352
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jnucmat.2011.02.038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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