Cluster Heat Bath Algorithm in Monte Carlo Simulations of Ising Models

TL;DR

This paper introduces a cluster heat bath algorithm for Monte Carlo simulations of Ising models, enhancing relaxation efficiency in complex systems by selecting cluster configurations based on Boltzmann weights, demonstrated on a 2D ANNNI model.

Contribution

The paper presents a novel cluster heat bath method that improves relaxation times in Monte Carlo simulations of complex Ising systems.

Findings

Enhanced relaxation in 2D ANNNI model

Method aligns with Boltzmann distribution

Potential applicability to other complex systems

Abstract

We have proposed a cluster heat bath method in Monte Carlo simulations of Ising models in which one of the possible spin configurations of a cluster is selected in accordance with its Boltzmann weight. We have argued that the method improves slow relaxation in complex systems and demonstrated it in an axial next-nearest-neighbor Ising(ANNNI) model in two-dimensions.