Microcanonical Approach to the Simulation of First-Order Phase Transitions

TL;DR

This paper introduces a microcanonical-based simulation method for first-order phase transitions that simplifies the process and improves efficiency over traditional flat-histogram techniques, especially for large systems.

Contribution

The paper presents a novel microcanonical approach that eliminates the need for iterative parameter tuning and long tunneling waits in simulating first-order phase transitions.

Findings

Accurate results for systems with over one million spins

Outperforms flat-histogram methods on large systems

Developed a cluster algorithm for the Potts model

Abstract

A generalization of the microcanonical ensemble suggests a simple strategy for the simulation of first order phase transitions. At variance with flat-histogram methods, there is no iterative parameters optimization, nor long waits for tunneling between the ordered and the disordered phases. We test the method in the standard benchmark: the Q-states Potts model (Q=10 in 2 dimensions and Q=4 in 3 dimensions), where we develop a cluster algorithm. We obtain accurate results for systems with more than one million of spins, outperforming flat-histogram methods that handle up to tens of thousands of spins.