Machine-Learning Studies on Spin Models

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 config...

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Bibliographic Details
Published in:Scientific reports 2020-02, Vol.10 (1), p.2177-2177, Article 2177
Main Authors: Shiina, Kenta, Mori, Hiroyuki, Okabe, Yutaka, Lee, Hwee Kuan
Format: Article
Language:English
Subjects:
639/766/259
639/766/530/2795
Classification
Humanities and Social Sciences
Learning algorithms
Machine learning
multidisciplinary
Phase transitions
Physics
Science
Science (multidisciplinary)
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-58263-5