Department of Physics, Tokyo Metropolitan University, Hachioji, Tokyo, 192-0397, Japan.
Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, 138671, Singapore, Singapore.
Sci Rep. 2020 Feb 7;10(1):2177. doi: 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.
随着机器学习的最新发展，Carrasquilla 和 Melko 提出了一种与传统自旋模型研究方法互补的范例。他们不是通过机器学习研究宏观物理量的热平均值，而是使用自旋构型来对相变的无序相和有序相进行分类。我们对这种方法进行了扩展和推广。我们关注的是长程相关函数的构型，而不是自旋构型本身，这使我们能够对多分量系统和具有矢量序参量的系统进行相同的处理。我们使用相同的技术分析 Berezinskii-Kosterlitz-Thouless（BKT）相变，将其分为三个相：无序相、BKT 相和有序相。我们还使用不同模型的训练数据对模型进行了分类。
