Machine-Learning Studies on Spin Models

Abstract

With the recent developments in machine learning, Carrasquilla and Melko have proposed a paradigm that is complementary to the conventional approach for the study of spin models. As an alternative to investigating the thermal average of macroscopic physical quantities, they have used the spin configurations for the classification of the disordered and ordered phases of a phase transition through machine learning. We extend and generalize this method. We focus on the configuration of the long-range correlation function instead of the spin configuration itself, which enables us to provide the same treatment to multi-component systems and the systems with a vector order parameter. We analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition with the same technique to classify three phases: the disordered, the BKT, and the ordered phases. We also present the classification of a model using the training data of a different model.

accepted for publication in Scientific Reports; main text + supplementary information

References in corpus (1)

• Machine learning vortices at the Kosterlitz-Thouless transition

Cited by in corpus (5)

• Machine-learning detection of the Berezinskii-Kosterlitz-Thouless transitions in the q-state clock models
• Machine-Learning Study using Improved Correlation Configuration and Application to Quantum Monte Carlo Simulation
• Machine-learning detection of the Berezinskii-Kosterlitz-Thouless transition and the second-order phase transition in the XXZ models
• Comprehensive studies on the universality of BKT transitions -- Machine-learning study, Monte Carlo simulation, and Level-spectroscopy method
• Machine-Learning Detection of the Berezinskii-Kosterlitz-Thouless Transitions