Work

Unveiling phase transitions with machine learning

Year: 2019

Type: article

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.

Source: Physical review. B./Physical review. B

DOI: 10.1103/physrevb.100.045129

Authors: Askery Canabarro, Felipe Fernandes Fanchini, André Luiz Malvezzi, Rodrigo Pereira, Rafael Chaves

Institutions: Universidade Federal do Rio Grande do Norte, Universidade Federal de Alagoas, Universidade Estadual Paulista (Unesp)

Language: English

Topic: Quantum many-body systems

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: bronze