arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2411.04567cond-mat.dis-nncond-mat.stat-mechphysics.comp-ph
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利用场变化和机器学习进行单副本自旋玻璃相检测

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

Ali Talebi, Mahsa Bagherikalhor, Behrouz Askari, G. Reza Jafari

AI总结:

本文提出一种利用单副本中自旋所经历场的变化(自发构型场)及机器学习算法来检测自旋玻璃系统不同相(铁磁、顺磁、混合相)的方法。

摘要:

Sherrington-Kirkpatrick 自旋玻璃模型使用副本对称方法来寻找系统的相变。在1979-1980年,Parisi 提出了基于副本对称破缺(RSB)的解决方案,这使他能够识别出自旋玻璃等复杂系统的潜在相。无论使用何种检测方法,系统的内在相都是存在的,无论是否考虑副本。我们引入了一种单副本的自旋玻璃相检测方法,利用系统构型中每个自旋所经历的场的变化。该方法聚焦于具有淬火随机耦合的单副本。每个自旋不可避免地观察到与其他自旋不同的场。我们的结果表明,自旋所经历的名为“自发构型场”的场的均值和方差是探索不同铁磁、顺磁和混合相的合适指标。为了用定义的指标对系统的不同相进行分类,我们开发了一种基于机器学习的算法来分析所需样本。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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