Shiina, K., Mori, H., Okabe, Y. et al. Machine-Learning Studies on Spin Models. Sci Rep 10, 2177 (2020). https://doi.org/10.1038/s41598-020-58263-5
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.
This work was supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion
of Science, Grant Number JP16K05480, Tokyo Metropolitan University, Japan, and core funding from the Bioinformatics Institute, ARES. KS is grateful to the A*STAR Research Attachment Programme (ARAP) of Singapore for financial support.