Artificial neural networks for density-functional optimizations in fermionic systems
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1038/s41598-018-37999-1 http://hdl.handle.net/11449/187347 |
Resumo: | In this work we propose an artificial neural network functional to the ground-state energy of fermionic interacting particles in homogeneous chains described by the Hubbard model. Our neural network functional was proven to have an excellent performance: it deviates from numerically exact calculations by less than 0.15% for a vast regime of interactions and for all the regimes of filling factors and magnetizations. When compared to analytical functionals, the neural functional was found to be more precise for all the regimes of parameters, being particularly superior at the weakly interacting regime: where the analytical parametrization fails the most, ~7%, against only ~0.1% for the neural network. We have also applied our homogeneous functional to finite, localized impurities and harmonically confined systems within density-functional theory (DFT) methods. The results show that while our artificial neural network approach is substantially more accurate than other equivalently simple and fast DFT treatments, it has similar performance than more costly DFT calculations and other independent many-body calculations, at a fraction of the computational cost. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Artificial neural networks for density-functional optimizations in fermionic systemsIn this work we propose an artificial neural network functional to the ground-state energy of fermionic interacting particles in homogeneous chains described by the Hubbard model. Our neural network functional was proven to have an excellent performance: it deviates from numerically exact calculations by less than 0.15% for a vast regime of interactions and for all the regimes of filling factors and magnetizations. When compared to analytical functionals, the neural functional was found to be more precise for all the regimes of parameters, being particularly superior at the weakly interacting regime: where the analytical parametrization fails the most, ~7%, against only ~0.1% for the neural network. We have also applied our homogeneous functional to finite, localized impurities and harmonically confined systems within density-functional theory (DFT) methods. The results show that while our artificial neural network approach is substantially more accurate than other equivalently simple and fast DFT treatments, it has similar performance than more costly DFT calculations and other independent many-body calculations, at a fraction of the computational cost.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Institute of Chemistry São Paulo State University UNESPInstitute of Chemistry São Paulo State University UNESPCNPq: 448220/2014-8Universidade Estadual Paulista (Unesp)Custódio, Caio A. [UNESP]Filletti, Érica R. [UNESP]França, Vivian V. [UNESP]2019-10-06T15:33:20Z2019-10-06T15:33:20Z2019-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/s41598-018-37999-1Scientific Reports, v. 9, n. 1, 2019.2045-2322http://hdl.handle.net/11449/18734710.1038/s41598-018-37999-12-s2.0-85061480156Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengScientific Reportsinfo:eu-repo/semantics/openAccess2021-10-23T12:04:38Zoai:repositorio.unesp.br:11449/187347Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:06:28.268850Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Artificial neural networks for density-functional optimizations in fermionic systems |
title |
Artificial neural networks for density-functional optimizations in fermionic systems |
spellingShingle |
Artificial neural networks for density-functional optimizations in fermionic systems Custódio, Caio A. [UNESP] |
title_short |
Artificial neural networks for density-functional optimizations in fermionic systems |
title_full |
Artificial neural networks for density-functional optimizations in fermionic systems |
title_fullStr |
Artificial neural networks for density-functional optimizations in fermionic systems |
title_full_unstemmed |
Artificial neural networks for density-functional optimizations in fermionic systems |
title_sort |
Artificial neural networks for density-functional optimizations in fermionic systems |
author |
Custódio, Caio A. [UNESP] |
author_facet |
Custódio, Caio A. [UNESP] Filletti, Érica R. [UNESP] França, Vivian V. [UNESP] |
author_role |
author |
author2 |
Filletti, Érica R. [UNESP] França, Vivian V. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Custódio, Caio A. [UNESP] Filletti, Érica R. [UNESP] França, Vivian V. [UNESP] |
description |
In this work we propose an artificial neural network functional to the ground-state energy of fermionic interacting particles in homogeneous chains described by the Hubbard model. Our neural network functional was proven to have an excellent performance: it deviates from numerically exact calculations by less than 0.15% for a vast regime of interactions and for all the regimes of filling factors and magnetizations. When compared to analytical functionals, the neural functional was found to be more precise for all the regimes of parameters, being particularly superior at the weakly interacting regime: where the analytical parametrization fails the most, ~7%, against only ~0.1% for the neural network. We have also applied our homogeneous functional to finite, localized impurities and harmonically confined systems within density-functional theory (DFT) methods. The results show that while our artificial neural network approach is substantially more accurate than other equivalently simple and fast DFT treatments, it has similar performance than more costly DFT calculations and other independent many-body calculations, at a fraction of the computational cost. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-06T15:33:20Z 2019-10-06T15:33:20Z 2019-12-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1038/s41598-018-37999-1 Scientific Reports, v. 9, n. 1, 2019. 2045-2322 http://hdl.handle.net/11449/187347 10.1038/s41598-018-37999-1 2-s2.0-85061480156 |
url |
http://dx.doi.org/10.1038/s41598-018-37999-1 http://hdl.handle.net/11449/187347 |
identifier_str_mv |
Scientific Reports, v. 9, n. 1, 2019. 2045-2322 10.1038/s41598-018-37999-1 2-s2.0-85061480156 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Scientific Reports |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1808128462681014272 |