AI Article Synopsis
- To study disordered systems, we need algorithms that can effectively navigate the slow dynamics caused by glassy effects.
- Irreversible Monte Carlo methods, which don't follow detailed balance, can enhance sampling speed in some scenarios.
- Our research implements an irreversible event-chain Monte Carlo for hard disks and introduces a new algorithm with collective particle swaps, achieving the best performance among existing Monte Carlo techniques and enabling the formation of dense jammed packings.
Article 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 continuously 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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| http://dx.doi.org/10.1103/PhysRevLett.133.028202 | DOI Listing |
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