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

Author

Listed:
  • Ali Talebi
  • Mahsa Bagherikalhor
  • Behrouz Askari
  • G Reza Jafari

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.

Suggested Citation

  • Ali Talebi & Mahsa Bagherikalhor & Behrouz Askari & G Reza Jafari, 2025. "Single replica spin-glass phase detection using field variation and machine learning," PLOS ONE, Public Library of Science, vol. 20(12), pages 1-14, December.
    • Handle: RePEc:plo:pone00:0335503
    DOI: 10.1371/journal.pone.0335503
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    References listed on IDEAS

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    1. Ji-Ho Park & Won Tae Kim & Woonjae Won & Jun-Ho Kang & Soogil Lee & Byong-Guk Park & Byoung S. Ham & Younghun Jo & Fabian Rotermund & Kab-Jin Kim, 2022. "Observation of spin-glass-like characteristics in ferrimagnetic TbCo through energy-level-selective approach," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
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