Unveiling phase transitions with machine learning

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorFederal University of Rio Grande Do Norte-
Autor(es): dc.contributorUniversidade Federal de Alagoas-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorCanabarro, Askery-
Autor(es): dc.creatorFanchini, Felipe Fernandes-
Autor(es): dc.creatorMalvezzi, André Luiz-
Autor(es): dc.creatorPereira, Rodrigo-
Autor(es): dc.creatorChaves, Rafael-
Data de aceite: dc.date.accessioned2025-08-21T23:05:39Z-
Data de disponibilização: dc.date.available2025-08-21T23:05:39Z-
Data de envio: dc.date.issued2022-04-30-
Data de envio: dc.date.issued2022-04-30-
Data de envio: dc.date.issued2019-07-22-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1103/PhysRevB.100.045129-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/232898-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/232898-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionInternational Institute of Physics Federal University of Rio Grande Do Norte-
Descrição: dc.descriptionGrupo de Física da Matéria Condensada Núcleo de Ciências Exatas NCEx Campus Arapiraca Universidade Federal de Alagoas-
Descrição: dc.descriptionFaculdade de Ciências Universidade Estadual Paulista-
Descrição: dc.descriptionDepartamento de Física Teórica e Experimental Federal University of Rio Grande Do Norte-
Descrição: dc.descriptionSchool of Science and Technology Federal University of Rio Grande Do Norte-
Descrição: dc.descriptionFaculdade de Ciências Universidade Estadual Paulista-
Idioma: dc.languageen-
Relação: dc.relationPhysical Review B-
???dc.source???: dc.sourceScopus-
Título: dc.titleUnveiling phase transitions with machine learning-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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