Event-chain Monte Carlo algorithms for three- and many-particle interactions

We generalize the rejection-free event-chain Monte Carlo algorithm from many particle systems with pairwise interactions to systems with arbitrary three- or many-particle interactions. We introduce generalized lifting probabilities between particles and obtain a general set of equations for lifting...

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Bibliographic Details
Published in:arXiv.org 2017-04
Main Authors: Harland, Julian, Michel, Manon, Kampmann, Tobias A, Kierfeld, Jan
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Beads
Computer simulation
Monte Carlo simulation
Particle interactions
Online Access:Get full text
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Summary:We generalize the rejection-free event-chain Monte Carlo algorithm from many particle systems with pairwise interactions to systems with arbitrary three- or many-particle interactions. We introduce generalized lifting probabilities between particles and obtain a general set of equations for lifting probabilities, the solution of which guarantees maximal global balance. We validate the resulting three-particle event-chain Monte Carlo algorithms on three different systems by comparison with conventional local Monte Carlo simulations: (i) a test system of three particles with a three-particle interaction that depends on the enclosed triangle area; (ii) a hard-needle system in two dimensions, where needle interactions constitute three-particle interactions of the needle end points; (iii) a semiflexible polymer chain with a bending energy, which constitutes a three-particle interaction of neighboring chain beads. The examples demonstrate that the generalization to many-particle interactions broadens the applicability of event-chain algorithms considerably.
ISSN:2331-8422
DOI:10.48550/arxiv.1611.09098