Exploring first-order phase transitions with population annealing

Abstract

Population annealing is a hybrid of sequential and Markov chain Monte Carlo methods geared towards the efficient parallel simulation of systems with complex free-energy landscapes. Systems with first-order phase transitions are among the problems in computational physics that are difficult to tackle with standard methods such as local-update simulations in the canonical ensemble, for example with the Metropolis algorithm. It is hence interesting to see whether such transitions can be more easily studied using population annealing. We report here our preliminary observations from population annealing runs for the two-dimensional Potts model with $q>4$, where it undergoes a first-order transition.

10 pages, 3 figures, 3 tables

References in corpus (2)

• Comparing Monte Carlo methods for finding ground states of Ising spin glasses: population annealing, simulated annealing and parallel tempering
• Evidence against a mean field description of short-range spin glasses revealed through thermal boundary conditions

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• Equilibrium Microcanonical Annealing for First-Order Phase Transitions
• Algorithm for the replica redistribution in the implementation of parallel annealing method on the hybrid supercomputer architecture
• Extracting partition function zeros from Fukui-Todo simulations