论文标题
自旋模型的机器学习研究
Machine-Learning Studies on Spin Models
论文作者
论文摘要
随着机器学习的最新发展，Carrasquilla和Melko提出了一种范式，该范式符合用于研究自旋模型的常规方法。作为研究宏观物理量的热平均值的一种替代方法，他们已经使用自旋构型通过机器学习对相位过渡的无序和有序阶段进行分类。我们扩展并推广此方法。我们专注于远程相关函数的配置，而不是旋转配置本身，这使我们能够为具有矢量顺序参数的多组分系统和系统提供相同的处理。我们用相同的技术分析了Berezinskii-Kosterlitz-thouless（BKT）过渡，以对三个阶段进行分类：无序，BKT和有序阶段。我们还使用不同模型的训练数据介绍了模型的分类。
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.
