A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive properties

Producción Científica

Detalles Bibliográficos
Autores: Álvarez Zapatero, Pablo, Vega Hierro, Andrés, Aguado Rodríguez, Andrés
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/49024
Acceso en línea:https://doi.org/10.1016/j.actamat.2021.117341
https://uvadoc.uva.es/handle/10324/49024
Access Level:acceso abierto
Palabra clave:Atomistic simulations
Simulaciones atomísticas
Artificial neural networks
Redes neuronales artificiales
Density functional theory
Teoría del funcional de densidad
Magnesium alloys
Aleaciones de magnesio
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spelling A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive propertiesÁlvarez Zapatero, PabloVega Hierro, AndrésAguado Rodríguez, AndrésAtomistic simulationsSimulaciones atomísticasArtificial neural networksRedes neuronales artificialesDensity functional theoryTeoría del funcional de densidadMagnesium alloysAleaciones de magnesioProducción CientíficaThe accurate description of the potential energy landscape of moderate-sized nanoparticles is a formidable task, but of paramount importance if one aims to characterize, in a realistic way, their physical and chemical properties. We present here a Neural Network potential able to predict structures of pure and mixed nanoparticles with an error in energy and forces of the order of chemical accuracy as compared with the values provided by the theoretical method used in the training process, in our case the density functional theory. The neural network is integrated into a basin-hopping algorithm which dynamically feeds the training process. The main ingredients of the neural network algorithm as well as the protocol used for its implementation and training are detailed, with particular emphasis on those aspects that make it so efficient and transferable. As a first test, we have applied it to the determination of the global minimum structures of ZnMg nanoalloys with up to 52 atoms and stoichiometries corresponding to MgZn and MgZn, of special interest in the context of anticorrosive coatings. We present and discuss the structural properties, chemical order, stability and pertinent electronic indicators, and we extract some conclusions on fundamental aspects that may be at the roots of the good performance of ZnMg nanoalloys as protective coatings. Finally, we comment on the step forward that the presented machine learning approach constitutes, both in the fact that it allows to accurately explore the potential energy surface of systems that other methodologies can not, and that it opens new prospects for a variety of problems in Materials Science.Ministerio de Economía, Industria y Competitividad (project PGC2018-093745-B-I00)Elsevier2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1016/j.actamat.2021.117341https://uvadoc.uva.es/handle/10324/49024reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://www.sciencedirect.com/science/article/pii/S1359645421007217?via%3Dihubinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:uvadoc.uva.es:10324/490242026-06-13T12:44:47Z
dc.title.none.fl_str_mv A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive properties
title A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive properties
spellingShingle A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive properties
Álvarez Zapatero, Pablo
Atomistic simulations
Simulaciones atomísticas
Artificial neural networks
Redes neuronales artificiales
Density functional theory
Teoría del funcional de densidad
Magnesium alloys
Aleaciones de magnesio
title_short A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive properties
title_full A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive properties
title_fullStr A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive properties
title_full_unstemmed A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive properties
title_sort A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive properties
dc.creator.none.fl_str_mv Álvarez Zapatero, Pablo
Vega Hierro, Andrés
Aguado Rodríguez, Andrés
author Álvarez Zapatero, Pablo
author_facet Álvarez Zapatero, Pablo
Vega Hierro, Andrés
Aguado Rodríguez, Andrés
author_role author
author2 Vega Hierro, Andrés
Aguado Rodríguez, Andrés
author2_role author
author
dc.subject.none.fl_str_mv Atomistic simulations
Simulaciones atomísticas
Artificial neural networks
Redes neuronales artificiales
Density functional theory
Teoría del funcional de densidad
Magnesium alloys
Aleaciones de magnesio
topic Atomistic simulations
Simulaciones atomísticas
Artificial neural networks
Redes neuronales artificiales
Density functional theory
Teoría del funcional de densidad
Magnesium alloys
Aleaciones de magnesio
description Producción Científica
publishDate 2021
dc.date.none.fl_str_mv 2021
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://doi.org/10.1016/j.actamat.2021.117341
https://uvadoc.uva.es/handle/10324/49024
url https://doi.org/10.1016/j.actamat.2021.117341
https://uvadoc.uva.es/handle/10324/49024
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S1359645421007217?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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