Unveiling phase transitions with machine learning
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/10.1103/PhysRevB.100.045129 http://hdl.handle.net/11449/186799 |
Resumo: | The classification of phase transitions is a central and challenging task in condensed matter physics. Typically, it relies on the identification of order parameters and the analysis of singularities in the free energy and its derivatives. Here, we propose an alternative framework to identify quantum phase transitions, employing both unsupervised and supervised machine learning techniques. Using the axial next-nearest-neighbor Ising (ANNNI) model as a benchmark, we show how unsupervised learning can detect three phases (ferromagnetic, paramagnetic, and a cluster of the antiphase with the floating phase) as well as two distinct regions within the paramagnetic phase. Employing supervised learning we show that transfer learning becomes possible: a machine trained only with nearest-neighbor interactions can learn to identify a new type of phase occurring when next-nearest-neighbor interactions are introduced. All our results rely on few- and low-dimensional input data (up to twelve lattice sites), thus providing a computational friendly and general framework for the study of phase transitions in many-body systems. |
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Repositório Institucional da UNESP |
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Unveiling phase transitions with machine learningThe classification of phase transitions is a central and challenging task in condensed matter physics. Typically, it relies on the identification of order parameters and the analysis of singularities in the free energy and its derivatives. Here, we propose an alternative framework to identify quantum phase transitions, employing both unsupervised and supervised machine learning techniques. Using the axial next-nearest-neighbor Ising (ANNNI) model as a benchmark, we show how unsupervised learning can detect three phases (ferromagnetic, paramagnetic, and a cluster of the antiphase with the floating phase) as well as two distinct regions within the paramagnetic phase. Employing supervised learning we show that transfer learning becomes possible: a machine trained only with nearest-neighbor interactions can learn to identify a new type of phase occurring when next-nearest-neighbor interactions are introduced. All our results rely on few- and low-dimensional input data (up to twelve lattice sites), thus providing a computational friendly and general framework for the study of phase transitions in many-body systems.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FFF's UniversalINCT-IQFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)UFALJohn Templeton FoundationSerrapilheira InstituteUniv Fed Rio Grande do Norte, Int Inst Phys, BR-59078970 Natal, RN, BrazilUniv Fed Alagoas, Grp Fis Mat Condensada, Nucl Ciencias Exatas NCEx, Campus Arapiraca, BR-57309005 Arapiraca, AL, BrazilUniv Estadual Paulista, Fac Ciencias, BR-17033360 Bauru, SP, BrazilUniv Fed Rio Grande do Norte, Dept Fis Teor & Expt, BR-59078970 Natal, RN, BrazilUniv Fed Rio Grande do Norte, Sch Sci & Technol, BR-59078970 Natal, RN, BrazilUniv Estadual Paulista, Fac Ciencias, BR-17033360 Bauru, SP, BrazilCNPq: 423713/2016-7FFF's Universal: 409309/2018-4FFF's Universal: 307172/2017-1FFF's Universal: 406574/2018-9FAPESP: 2019/05445-7John Templeton Foundation: 61084Serrapilheira Institute: Serra-1708-15763Amer Physical SocUniv Fed Rio Grande do NorteUniv Fed AlagoasUniversidade Estadual Paulista (Unesp)Canabarro, AskeryFanchini, Felipe Fernandes [UNESP]Malvezzi, Andre Luiz [UNESP]Pereira, RodrigoChaves, Rafael2019-10-06T05:29:16Z2019-10-06T05:29:16Z2019-07-22info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13http://dx.doi.org/10.1103/PhysRevB.100.045129Physical Review B. College Pk: Amer Physical Soc, v. 100, n. 4, 13 p., 2019.2469-9950http://hdl.handle.net/11449/18679910.1103/PhysRevB.100.045129WOS:00047668800000588848904721934740000-0003-3297-905XWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPhysical Review Binfo:eu-repo/semantics/openAccess2024-04-25T17:40:09Zoai:repositorio.unesp.br:11449/186799Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:09:38.231636Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Unveiling phase transitions with machine learning |
title |
Unveiling phase transitions with machine learning |
spellingShingle |
Unveiling phase transitions with machine learning Canabarro, Askery |
title_short |
Unveiling phase transitions with machine learning |
title_full |
Unveiling phase transitions with machine learning |
title_fullStr |
Unveiling phase transitions with machine learning |
title_full_unstemmed |
Unveiling phase transitions with machine learning |
title_sort |
Unveiling phase transitions with machine learning |
author |
Canabarro, Askery |
author_facet |
Canabarro, Askery Fanchini, Felipe Fernandes [UNESP] Malvezzi, Andre Luiz [UNESP] Pereira, Rodrigo Chaves, Rafael |
author_role |
author |
author2 |
Fanchini, Felipe Fernandes [UNESP] Malvezzi, Andre Luiz [UNESP] Pereira, Rodrigo Chaves, Rafael |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Univ Fed Rio Grande do Norte Univ Fed Alagoas Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Canabarro, Askery Fanchini, Felipe Fernandes [UNESP] Malvezzi, Andre Luiz [UNESP] Pereira, Rodrigo Chaves, Rafael |
description |
The classification of phase transitions is a central and challenging task in condensed matter physics. Typically, it relies on the identification of order parameters and the analysis of singularities in the free energy and its derivatives. Here, we propose an alternative framework to identify quantum phase transitions, employing both unsupervised and supervised machine learning techniques. Using the axial next-nearest-neighbor Ising (ANNNI) model as a benchmark, we show how unsupervised learning can detect three phases (ferromagnetic, paramagnetic, and a cluster of the antiphase with the floating phase) as well as two distinct regions within the paramagnetic phase. Employing supervised learning we show that transfer learning becomes possible: a machine trained only with nearest-neighbor interactions can learn to identify a new type of phase occurring when next-nearest-neighbor interactions are introduced. All our results rely on few- and low-dimensional input data (up to twelve lattice sites), thus providing a computational friendly and general framework for the study of phase transitions in many-body systems. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-06T05:29:16Z 2019-10-06T05:29:16Z 2019-07-22 |
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/10.1103/PhysRevB.100.045129 Physical Review B. College Pk: Amer Physical Soc, v. 100, n. 4, 13 p., 2019. 2469-9950 http://hdl.handle.net/11449/186799 10.1103/PhysRevB.100.045129 WOS:000476688000005 8884890472193474 0000-0003-3297-905X |
url |
http://dx.doi.org/10.1103/PhysRevB.100.045129 http://hdl.handle.net/11449/186799 |
identifier_str_mv |
Physical Review B. College Pk: Amer Physical Soc, v. 100, n. 4, 13 p., 2019. 2469-9950 10.1103/PhysRevB.100.045129 WOS:000476688000005 8884890472193474 0000-0003-3297-905X |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Physical Review B |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
13 |
dc.publisher.none.fl_str_mv |
Amer Physical Soc |
publisher.none.fl_str_mv |
Amer Physical Soc |
dc.source.none.fl_str_mv |
Web of Science 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 |
|
_version_ |
1808129166552334336 |