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1992•Physical review. B, Condensed matterRequires access

Hybrid Monte Carlo method for condensed-matter systems

B. Mehlig, Dieter W. Heermann, Bruce M. Forrest

Open publisher page 285 citations

Abstract

In this paper the static properties of the hybrid Monte Carlo algorithm are studied in the context of condensed-matter systems. The algorithm is used to simulate a Lennard-Jones liquid near the coexistence region. The hybrid Monte Carlo algorithm generates a canonical distribution in configuration space, permitting the application of the Ferrenberg-Swendsen extrapolation scheme. Moreover, it is an exact algorithm: The configurational averages prove to be independent of the step size, and the algorithm does not suffer from numerical instabilities due to finite step size.

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What this paper is about

In this paper the static properties of the hybrid Monte Carlo algorithm are studied in the context of condensed-matter systems. The algorithm is used to simulate a Lennard-Jones liquid near the coexistence region. The hybrid Monte Carlo algorithm generates a canonical distribution in configuration space, permitting the application of the Ferrenberg-Swendsen extrapolation scheme. Moreover, it is an exact algorithm: The configurational averages prove to be independent of the step size, and the algorithm does not suffer from numerical instabilities due to finite step size.

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OpenAlex reports 285 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this paper the static properties of the hybrid Monte Carlo algorithm are studied in the context of condensed-matter systems. The algorithm is used to simulate a Lennard-Jones liquid near the coexistence region. The hybrid Monte Carlo algorithm generates a canonical distribution in configuration space, permitting the application of the Ferrenberg-Swendsen extrapolation scheme. Moreover, it is an exact algorithm: The configurational averages prove to be independent of the step size, and the algorithm does not suffer from numerical instabilities due to finite step size.

Key concepts: Monte Carlo method, Extrapolation, Statistical physics, Dynamic Monte Carlo method, Hybrid Monte Carlo, Monte Carlo molecular modeling, Monte Carlo method in statistical physics, Monte Carlo algorithm

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