Unveiling phase transitions with machine learning

Canabarro, Askery Alexandre Canabarro Barbosa da; Fanchini, Felipe Fernandes; Malvezzi, André Luiz; Pereira, Rodrigo Gonçalves; Araújo, Rafael Chaves Souto

Unveiling phase transitions with machine learning

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dc.contributor.author	Canabarro, Askery Alexandre Canabarro Barbosa da
dc.contributor.author	Fanchini, Felipe Fernandes
dc.contributor.author	Malvezzi, André Luiz
dc.contributor.author	Pereira, Rodrigo Gonçalves
dc.contributor.author	Araújo, Rafael Chaves Souto
dc.date.accessioned	2020-09-21T22:44:14Z
dc.date.available	2020-09-21T22:44:14Z
dc.date.issued	2019-07-22
dc.description.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	pt_BR
dc.identifier.citation	CANABARRO, Askery; FANCHINI, Felipe Fernandes; MALVEZZI, André Luiz; PEREIRA, Rodrigo; CHAVES, Rafael. Unveiling phase transitions with machine learning. Physical Review B, [s.l.], v. 100, n. 4, p. 045129, 22 jul. 2019. Disponível em: https://journals.aps.org/prb/abstract/10.1103/PhysRevB.100.045129. Acesso em: 15 set. 2020. http://dx.doi.org/10.1103/physrevb.100.045129.	pt_BR
dc.identifier.doi	10.1103/PhysRevB.100.045129
dc.identifier.issn	2469-9950
dc.identifier.issn	2469-9969
dc.identifier.uri	https://repositorio.ufrn.br/jspui/handle/123456789/30141
dc.language	en	pt_BR
dc.publisher	American Physical Society	pt_BR
dc.subject	Condensed matter physics	pt_BR
dc.subject	Many-body systems	pt_BR
dc.title	Unveiling phase transitions with machine learning	pt_BR
dc.type	article	pt_BR