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

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

PLOS ONE, 2025, vol. 20, issue 12, 1-14

Abstract: The Sherrington-Kirkpatrick (SK) spin-glass model exhibits well-studied phase transitions that are mostly established using replica-based methods. Regardless of the method used for detection, the intrinsic phase of a system exists whether or not replicas are considered. Therefore, in this study, we propose a novel method for phase detection based on the variation of the local field experienced by each spin in a configuration of a single replica. The mean and the variance of these local fields are powerful indicators that effectively distinguish different phases, including ferromagnetic, paramagnetic, and spin-glass phases. By analyzing the mean and variance of these local fields, we develop a machine learning algorithm to generate the phase diagram, which shows strong agreement with the theoretical solutions for the SK model. This algorithm offers a more computationally efficient approach for phase detection in spin-glass systems.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0335503

DOI: 10.1371/journal.pone.0335503

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