Machine learning applications in phase transitions
Abstract
Neural networks in machine learning are highly capable of classifying data and recognizing graphics, and have been widely used in statistical physics, particularly in phase transitions. In this paper, we review recent developments in machine learning algorithms in the application of primary phase transition models. We first introduce the background of modern machine learning algorithms and related pioneering work in equilibrium phase transitions. Second, by taking directed percolation as an example of a typical non-equilibrium phase transition model, we present some of the latest research findings of our group. The research involves supervised, unsupervised, and semi-supervised learning for classifying phases, predicting critical points, and measuring critical exponents. Thereafter, we elaborate on related work on machine learning algorithms in phase transition research in quantum many-body theory, soft matter physics, and high-energy physics. Lastly, corresponding discussions and prospects are presented.
