Irreversible Monte Carlo algorithms for hard disk glasses: from event-chain to collective swaps

Abstract

Equilibrium sampling of the configuration space in disordered systems requires algorithms that bypass the glassy slowing down of the physical dynamics. Irreversible Monte Carlo algorithms breaking detailed balance successfully accelerate sampling in some systems. We first implement an irreversible event-chain Monte Carlo algorithm in a model of polydisperse hard disks. The effect of collective translational moves marginally affects the dynamics and results in a modest speedup that decreases with density. We then propose an irreversible algorithm performing collective particle swaps which outperforms all known Monte Carlo algorithms. We show that these collective swaps can also be used to prepare very dense jammed packings of disks.

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• Exceptionally Dense and Resilient Polydisperse Disk Packings
• Irreversible Boltzmann samplers in dense liquids: weak-coupling approximation and mode-coupling theory
• Characterising the slow dynamics of the swap Monte Carlo algorithm
• Irreversible swap algorithms for soft sphere glasses
• Clever algorithms for glasses work by time reparametrization
• Bosonized one-dimensional quantum systems through enhanced event-chain Monte Carlo
• Computational Methods toward Ultrastable Glasses
• Predicting random close packing of binary hard-disk mixtures via third-virial-based parameters