标题： 使用场变化和机器学习的单副本自旋玻璃相检测 显示英文标题

标题： Single replica spin-glass phase detection using field variation and machine learning

摘要： Sherrington-Kirkpatrick 自旋玻璃模型使用复制对称性方法来寻找系统的相变。在1979年至1980年期间，Parisi提出了基于复制对称性破缺（RSB）的解决方案，这使他能够识别复杂系统（如自旋玻璃）的基本相态。无论采用何种检测方法，系统的固有相态是否存在并不依赖于是否考虑复制。我们引入了一种单复制方法来检测自旋玻璃的相态，该方法关注系统配置中每个自旋经历的场的变化。此方法聚焦于具有冻结随机耦合的单一复制。每个自旋不可避免地会观察到与其他自旋不同的场。我们的结果显示，被称为“自发构型场”的自旋所经历的场的均值和方差是探索铁磁相、顺磁相和混合相的良好指标。为了用定义的指标分类系统的不同相态，我们开发了一种基于机器学习的算法来分析所需的样本。 显示英文摘要

摘要： The Sherrington-Kirkpatrick spin-glass model used the replica symmetry method to find the phase transition of the system. In 1979-1980, Parisi proposed a solution based on replica symmetry breaking (RSB), which allowed him to identify the underlying phases of complex systems such as spin-glasses. Regardless of the method used for detection, the intrinsic phase of a system exists whether or not replicas are considered. We introduce a single replica method of spin-glass phase detection using the field's variation experienced by each spin in a system configuration. This method focuses on a single replica with quenched random couplings. Each spin inevitably observes a different field from the others. Our results show that the mean and variance of fields named "Spontaneous Configurational Field" experienced by spins are suitable indicators to explore different ferromagnetic, paramagnetic, and mixed phases. To classify different phases of the system with defined indicators we have developed an algorithm based on machine learning to analyze the desired samples.