期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 108, 期 45, 页码 E1009-E1018出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1106094108
关键词
expanded ensembles; Markov chain Monte Carlo; Metropolis-Hastings; molecular dynamics
资金
- US Department of Energy (DOE) by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
- Helios Solar Energy Research Center
- Office of Science, Office of Basic Energy Sciences of the DOE [DE-AC02-05CH11231]
- Argonne National Laboratory
- California Institute for Quantitative Biosciences (QB3) at the University of California, Berkeley
- DOE by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
- University of Chicago Argonne, LLC, Operator of Argonne National Laboratory (Argonne). Argonne
- USDE Office of Science laboratory [DE-AC02-06CH11357]
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.
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