Characterizing spin ordering via maximal row correlation in classical spin models

An order parameter, termed the maximal row correlation, is proposed for classical spin systems. Monte Carlo simulations on various Potts models suggest that this order parameter is applicable to a broad range of spin systems, including those defined on irregular lattices, systems with frustration, and systems exhibiting partial orders, provided some degree of spin ordering is present. This approach offers a unified framework for investigating phase transitions in such complex systems. The associated critical exponents are estimated via finite-size scaling analysis and show good agreement with established values.

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Publication Details

Journal
Physical review. E
Published
2025-09-16
DOI
https://doi.org/10.1103/qqyv-jhgg
Primary Topic
Theoretical and Computational Physics
Type
article
Field-Weighted Citation Impact
0.00

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article

Characterizing spin ordering via maximal row correlation in classical spin models

Yin Zhong, Hantao Lu, Yong-Yi Tang
Physical review. E
Theoretical and Computational Physics
article

Characterizing spin ordering via maximal row correlation in classical spin models

Yin Zhong, Hantao Lu, Yong-Yi Tang
article en

Abstract

An order parameter, termed the maximal row correlation, is proposed for classical spin systems. Monte Carlo simulations on various Potts models suggest that this order parameter is applicable to a broad range of spin systems, including those defined on irregular lattices, systems with frustration, and systems exhibiting partial orders, provided some degree of spin ordering is present. This approach offers a unified framework for investigating phase transitions in such complex systems. The associated critical exponents are estimated via finite-size scaling analysis and show good agreement with established values.

Physical review. EVol. 112(3)
Lanzhou University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 85%
Theoretical and Computational Physics
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