Machine-Learning Detection of the Berezinskii-Kosterlitz-Thouless Transitions

TL;DR

This paper introduces new machine-learning methods that successfully detect Berezinskii-Kosterlitz-Thouless (BKT) transitions in spin models, overcoming previous challenges related to the lack of order parameters and symmetry breaking.

Contribution

The paper presents two novel machine-learning techniques, temperature-identification and phase-classification, for detecting BKT transitions without prior model knowledge or feature engineering.

Findings

Successfully detected BKT transitions in q-state clock and XXZ models

Overcame difficulties due to absence of trivial order parameters

Enhanced machine-learning methods for topological phase transitions

Abstract

The Berezinskii-Kosterlitz-Thouless (BKT) transition is a typical topological phase transition defined between binding and unbinding states of vortices and antivortices, which is not accompanied by spontaneous symmetry breaking. It is known that the BKT transition is difficult to detect from thermodynamic quantities such as specific heat and magnetic susceptibility because of the absence of anomaly in free energy and significant finite-size effects. Therefore, methods based on statistical mechanics which are commonly used to discuss phase transitions cannot be easily applied to the BKT transition. In recent years, several attempts to detect the BKT transition using machine-learning methods based on image recognition techniques have been reported. However, it has turned out that the detection is difficult even for machine learning methods because of the absence of trivial order parameters and symmetry breaking. Most of the methods proposed so far require prior knowledge about the models and/or preprocessing of input data for feature engineering, which is problematic in terms of the general applicability. In this article, we introduce recent development of the machine-learning methods to detect the BKT transitions in several spin models. Specifically, we demonstrate the success of two new methods named temperature-identification method and phase-classification method for detecting the BKT transitions in the q-state clock model and the XXZ model. This progress is expected to sublimate the machine-learning-based study of spin models for exploring new physics beyond simple benchmark test.