Characterizing spin ordering via maximal row correlation in classical spin models

TL;DR

This paper introduces the maximal row correlation as a new order parameter for classical spin models, enabling unified analysis of phase transitions across diverse and complex spin systems.

Contribution

It proposes a novel order parameter applicable to various complex spin systems and demonstrates its effectiveness through Monte Carlo simulations and finite-size scaling.

Findings

Effective in irregular lattices and frustrated systems

Accurate estimation of critical exponents

Unified framework for phase transition analysis

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.