Predicting vacancy migration energies in lattice-free environments using artificial neural networks

Autores
Castin, Nicolas; Fernández, J. R.; Pasianot, Roberto Cesar
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We propose a methodology for predicting migration energies associated to the migration of single atoms towards vacant sites, using artificial neural networks. The novelty of the approach, which has already been proven efficient for bulk materials (e.g. bcc or fcc Fe-based alloys), is to allow for any structure, without restriction to a specific lattice. The proposed technique is designed in conjunction with a novel kind of lattice-free atomistic kinetic Monte Carlo model. The idea is to avoid as much as possible heavy atomistic simulations, e.g. static relaxation or general methods for finding transition paths. Such calculations, however, are applied once per Monte Carlo event, when a selected event is applied. The objective of this work is thus to propose a methodology for defining migration events at every step of the simulation, and at the same time assigning a frequency of occurrence to them (using artificial neural networks), in short computing times. We demonstrate the feasibility of this new concept by designing neural networks for predicting vacancy migration energies near grain boundaries in bcc FeCr alloys.
Fil: Castin, Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Nuclear Materials Science Institute. Belgian Nuclear Research Centre; Bélgica
Fil: Fernández, J. R.. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Unidad de Actividad de Materiales (CAC); Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina
Fil: Pasianot, Roberto Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Unidad de Actividad de Materiales (CAC); Argentina
Materia
Kinetic Montecarlo
Lattice Free
Artificial Neural Networks
Diffusion
Grain Boundaries
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:11336/32761
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/32761
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Predicting vacancy migration energies in lattice-free environments using artificial neural networksCastin, NicolasFernández, J. R.Pasianot, Roberto CesarKinetic MontecarloLattice FreeArtificial Neural NetworksDiffusionGrain Boundarieshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We propose a methodology for predicting migration energies associated to the migration of single atoms towards vacant sites, using artificial neural networks. The novelty of the approach, which has already been proven efficient for bulk materials (e.g. bcc or fcc Fe-based alloys), is to allow for any structure, without restriction to a specific lattice. The proposed technique is designed in conjunction with a novel kind of lattice-free atomistic kinetic Monte Carlo model. The idea is to avoid as much as possible heavy atomistic simulations, e.g. static relaxation or general methods for finding transition paths. Such calculations, however, are applied once per Monte Carlo event, when a selected event is applied. The objective of this work is thus to propose a methodology for defining migration events at every step of the simulation, and at the same time assigning a frequency of occurrence to them (using artificial neural networks), in short computing times. We demonstrate the feasibility of this new concept by designing neural networks for predicting vacancy migration energies near grain boundaries in bcc FeCr alloys.Fil: Castin, Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Nuclear Materials Science Institute. Belgian Nuclear Research Centre; BélgicaFil: Fernández, J. R.. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Unidad de Actividad de Materiales (CAC); Argentina. Universidad Nacional de San Martín. Instituto Sabato; ArgentinaFil: Pasianot, Roberto Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Unidad de Actividad de Materiales (CAC); ArgentinaElsevier2013-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/32761Pasianot, Roberto Cesar; Fernández, J. R.; Castin, Nicolas; Predicting vacancy migration energies in lattice-free environments using artificial neural networks; Elsevier; Computational Materials Science; 84; 12-2013; 217-2250927-0256CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0927025613007659info:eu-repo/semantics/altIdentifier/doi/10.1016/j.commatsci.2013.12.016info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:22:24Zoai:ri.conicet.gov.ar:11336/32761instacron: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:34982025-10-22 11:22:24.667CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Predicting vacancy migration energies in lattice-free environments using artificial neural networks
title Predicting vacancy migration energies in lattice-free environments using artificial neural networks
spellingShingle Predicting vacancy migration energies in lattice-free environments using artificial neural networks
Castin, Nicolas
Kinetic Montecarlo
Lattice Free
Artificial Neural Networks
Diffusion
Grain Boundaries
title_short Predicting vacancy migration energies in lattice-free environments using artificial neural networks
title_full Predicting vacancy migration energies in lattice-free environments using artificial neural networks
title_fullStr Predicting vacancy migration energies in lattice-free environments using artificial neural networks
title_full_unstemmed Predicting vacancy migration energies in lattice-free environments using artificial neural networks
title_sort Predicting vacancy migration energies in lattice-free environments using artificial neural networks
dc.creator.none.fl_str_mv Castin, Nicolas
Fernández, J. R.
Pasianot, Roberto Cesar
author Castin, Nicolas
author_facet Castin, Nicolas
Fernández, J. R.
Pasianot, Roberto Cesar
author_role author
author2 Fernández, J. R.
Pasianot, Roberto Cesar
author2_role author
author
dc.subject.none.fl_str_mv Kinetic Montecarlo
Lattice Free
Artificial Neural Networks
Diffusion
Grain Boundaries
topic Kinetic Montecarlo
Lattice Free
Artificial Neural Networks
Diffusion
Grain Boundaries
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We propose a methodology for predicting migration energies associated to the migration of single atoms towards vacant sites, using artificial neural networks. The novelty of the approach, which has already been proven efficient for bulk materials (e.g. bcc or fcc Fe-based alloys), is to allow for any structure, without restriction to a specific lattice. The proposed technique is designed in conjunction with a novel kind of lattice-free atomistic kinetic Monte Carlo model. The idea is to avoid as much as possible heavy atomistic simulations, e.g. static relaxation or general methods for finding transition paths. Such calculations, however, are applied once per Monte Carlo event, when a selected event is applied. The objective of this work is thus to propose a methodology for defining migration events at every step of the simulation, and at the same time assigning a frequency of occurrence to them (using artificial neural networks), in short computing times. We demonstrate the feasibility of this new concept by designing neural networks for predicting vacancy migration energies near grain boundaries in bcc FeCr alloys.
Fil: Castin, Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Nuclear Materials Science Institute. Belgian Nuclear Research Centre; Bélgica
Fil: Fernández, J. R.. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Unidad de Actividad de Materiales (CAC); Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina
Fil: Pasianot, Roberto Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Unidad de Actividad de Materiales (CAC); Argentina
description We propose a methodology for predicting migration energies associated to the migration of single atoms towards vacant sites, using artificial neural networks. The novelty of the approach, which has already been proven efficient for bulk materials (e.g. bcc or fcc Fe-based alloys), is to allow for any structure, without restriction to a specific lattice. The proposed technique is designed in conjunction with a novel kind of lattice-free atomistic kinetic Monte Carlo model. The idea is to avoid as much as possible heavy atomistic simulations, e.g. static relaxation or general methods for finding transition paths. Such calculations, however, are applied once per Monte Carlo event, when a selected event is applied. The objective of this work is thus to propose a methodology for defining migration events at every step of the simulation, and at the same time assigning a frequency of occurrence to them (using artificial neural networks), in short computing times. We demonstrate the feasibility of this new concept by designing neural networks for predicting vacancy migration energies near grain boundaries in bcc FeCr alloys.
publishDate 2013
dc.date.none.fl_str_mv 2013-12
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/11336/32761
Pasianot, Roberto Cesar; Fernández, J. R.; Castin, Nicolas; Predicting vacancy migration energies in lattice-free environments using artificial neural networks; Elsevier; Computational Materials Science; 84; 12-2013; 217-225
0927-0256
CONICET Digital
CONICET
url http://hdl.handle.net/11336/32761
identifier_str_mv Pasianot, Roberto Cesar; Fernández, J. R.; Castin, Nicolas; Predicting vacancy migration energies in lattice-free environments using artificial neural networks; Elsevier; Computational Materials Science; 84; 12-2013; 217-225
0927-0256
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/S0927025613007659
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.commatsci.2013.12.016
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
publisher.none.fl_str_mv Elsevier
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 12.982451

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