Estudo do estado fundamental de aglomerados de silício via redes neurais

Detalhes bibliográficos
Autor(a) principal: Lemes,Maurício Ruv
Data de Publicação: 2002
Outros Autores: Dal Pino Júnior,Arnaldo
Tipo de documento: Artigo
Idioma: por
Título da fonte: Química Nova (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422002000400005
Resumo: We introduce a global optimization method based on the cooperation between an Artificial Neural Net (ANN) and Genetic Algorithm (GA). We have used ANN to select the initial population for the GA. We have tested the new method to predict the ground-state geometry of silicon clusters. We have described the clusters as a piling of plane structures. We have trained three ANN architectures and compared their results with those of pure GA. ANN strongly reduces the total computational time. For Si10, it gained a factor of 5 in search speed. This method can be easily extended to other optimization problems.
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spelling Estudo do estado fundamental de aglomerados de silício via redes neuraissilicon clustersgenetic algoritmneural networkWe introduce a global optimization method based on the cooperation between an Artificial Neural Net (ANN) and Genetic Algorithm (GA). We have used ANN to select the initial population for the GA. We have tested the new method to predict the ground-state geometry of silicon clusters. We have described the clusters as a piling of plane structures. We have trained three ANN architectures and compared their results with those of pure GA. ANN strongly reduces the total computational time. For Si10, it gained a factor of 5 in search speed. This method can be easily extended to other optimization problems.Sociedade Brasileira de Química2002-07-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422002000400005Química Nova v.25 n.4 2002reponame:Química Nova (Online)instname:Sociedade Brasileira de Química (SBQ)instacron:SBQ10.1590/S0100-40422002000400005info:eu-repo/semantics/openAccessLemes,Maurício RuvDal Pino Júnior,Arnaldopor2002-08-26T00:00:00Zoai:scielo:S0100-40422002000400005Revistahttps://www.scielo.br/j/qn/ONGhttps://old.scielo.br/oai/scielo-oai.phpquimicanova@sbq.org.br1678-70640100-4042opendoar:2002-08-26T00:00Química Nova (Online) - Sociedade Brasileira de Química (SBQ)false
dc.title.none.fl_str_mv Estudo do estado fundamental de aglomerados de silício via redes neurais
title Estudo do estado fundamental de aglomerados de silício via redes neurais
spellingShingle Estudo do estado fundamental de aglomerados de silício via redes neurais
Lemes,Maurício Ruv
silicon clusters
genetic algoritm
neural network
title_short Estudo do estado fundamental de aglomerados de silício via redes neurais
title_full Estudo do estado fundamental de aglomerados de silício via redes neurais
title_fullStr Estudo do estado fundamental de aglomerados de silício via redes neurais
title_full_unstemmed Estudo do estado fundamental de aglomerados de silício via redes neurais
title_sort Estudo do estado fundamental de aglomerados de silício via redes neurais
author Lemes,Maurício Ruv
author_facet Lemes,Maurício Ruv
Dal Pino Júnior,Arnaldo
author_role author
author2 Dal Pino Júnior,Arnaldo
author2_role author
dc.contributor.author.fl_str_mv Lemes,Maurício Ruv
Dal Pino Júnior,Arnaldo
dc.subject.por.fl_str_mv silicon clusters
genetic algoritm
neural network
topic silicon clusters
genetic algoritm
neural network
description We introduce a global optimization method based on the cooperation between an Artificial Neural Net (ANN) and Genetic Algorithm (GA). We have used ANN to select the initial population for the GA. We have tested the new method to predict the ground-state geometry of silicon clusters. We have described the clusters as a piling of plane structures. We have trained three ANN architectures and compared their results with those of pure GA. ANN strongly reduces the total computational time. For Si10, it gained a factor of 5 in search speed. This method can be easily extended to other optimization problems.
publishDate 2002
dc.date.none.fl_str_mv 2002-07-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422002000400005
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422002000400005
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv 10.1590/S0100-40422002000400005
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Química
publisher.none.fl_str_mv Sociedade Brasileira de Química
dc.source.none.fl_str_mv Química Nova v.25 n.4 2002
reponame:Química Nova (Online)
instname:Sociedade Brasileira de Química (SBQ)
instacron:SBQ
instname_str Sociedade Brasileira de Química (SBQ)
instacron_str SBQ
institution SBQ
reponame_str Química Nova (Online)
collection Química Nova (Online)
repository.name.fl_str_mv Química Nova (Online) - Sociedade Brasileira de Química (SBQ)
repository.mail.fl_str_mv quimicanova@sbq.org.br
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