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Title: Unveiling phase transitions with machine learning
Authors: Canabarro, Askery Alexandre Canabarro Barbosa da
Fanchini, Felipe Fernandes
Malvezzi, André Luiz
Pereira, Rodrigo Gonçalves
Araújo, Rafael Chaves Souto
Keywords: Condensed matter physics;Many-body systems
Issue Date: 22-Jul-2019
Publisher: American Physical Society
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: Acesso em: 15 set. 2020.
Portuguese Abstract: 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
ISSN: 2469-9950
Appears in Collections:CCET - DFTE - Artigos publicados em periódicos
ECT - Artigos publicados em periódicos
IIF - Artigos publicados em periódicos

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