Modified Monte Carlo method with the heat bath algorithm for a model cuprate

2025/10/09 by Yu. D. Panov, V. A. Ulitko, Panov, Yu. D. +5 · 1 citation
Engineering · Materials Science · Physics and Astronomy · #FOS: Physical sciences #Phase Equilibria and Thermodynamics #Solidification and crystal growth phenomena #Superconductivity (cond-mat.supr-con) #Theoretical and Computational Physics

paper · pdf · doi:10.48550/arxiv.2510.08289

openalex publication_date 2025/10/09 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28

Abstract

The results of numerical simulation using a modified Monte Carlo method with a heat bath algorithm for the pseudospin model of cuprates are presented. The temperature phase diagrams are constructed for various degrees of doping and for various parameters of the model, and the effect of local correlations on the critical temperatures of the model cuprate is investigated. It is shown that, in qualitative agreement with the results of the mean field, the heat bath algorithm leads to a significant decrease in the estimate of critical temperatures due to more complete accounting of fluctuations, and also makes it possible to detect phase inhomogeneous states. The possibility of using machine learning to accelerate the heat bath algorithm is discussed.

Citations

• Jahn-Teller magnets

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• Predicting parameters of a model cuprate superconductor using machine learning

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