Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation

Metropolis Monte Carlo simulation is a powerful tool for studying the equilibrium properties of matter. In complex condensed-phase systems, however, it is difficult to design Monte Carlo moves with high acceptance probabilities that also rapidly sample uncorrelated configurations. Here, we introduce...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2011-11, Vol.108 (45), p.E1009-E1018
Main Authors: Nilmeier, Jerome P, Crooks, Gavin E, Minh, David D. L, Chodera, John D
Format: Article
Language:English
Subjects:
Biological Sciences
Correlation analysis
energy
expanded ensembles
Markov chain Monte Carlo
MATHEMATICS AND COMPUTING
Metropolis-Hastings
Models, Theoretical
molecular dynamics
Monte Carlo Method
Monte Carlo simulation
Physical Sciences
PNAS Plus
probability
Probability distribution
Thermodynamics
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Summary:Metropolis Monte Carlo simulation is a powerful tool for studying the equilibrium properties of matter. In complex condensed-phase systems, however, it is difficult to design Monte Carlo moves with high acceptance probabilities that also rapidly sample uncorrelated configurations. Here, we introduce a new class of moves based on nonequilibrium dynamics: Candidate configurations are generated through a finite-time process in which a system is actively driven out of equilibrium, and accepted with criteria that preserve the equilibrium distribution. The acceptance rule is similar to the Metropolis acceptance probability, but related to the nonequilibrium work rather than the instantaneous energy difference. Our method is applicable to sampling from both a single thermodynamic state or a mixture of thermodynamic states, and allows both coordinates and thermodynamic parameters to be driven in nonequilibrium proposals. Whereas generating finite-time switching trajectories incurs an additional cost, driving some degrees of freedom while allowing others to evolve naturally can lead to large enhancements in acceptance probabilities, greatly reducing structural correlation times. Using nonequilibrium driven processes vastly expands the repertoire of useful Monte Carlo proposals in simulations of dense solvated systems.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1106094108