Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation

By Jerome P. Nilmeier, Gavin E. Crooks, David D. L. Minh, John D. Chodera. 2011

104 citations recorded by the source corpus.

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.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry, Statistical and Nonlinear Physics, Probabilistic & Bayesian Methods

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Citations in the corpus

Stochastic Normalizing Flows (2020, methodology): “The reverse step is de-ﬁned by ( x t +1 , − v t +1 ) → ( x t , − v t ) and is realized by the noise variables (Nilmeier et al., 2011): The log path probability ratio is (Suppl.”
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics (2016, background): “• Nonequilibrium candidate Monte Carlo (NCMC) [26], where an external field is changed during the course of dynamics and the resulting nonequilibrium proposal accepted or rejected to preserve the equilibrium distribution.”
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning (2019, unknown): “Wesimulatedadensesystemoftwo-dimensionalparticlesconfinedtoabox,assuggestedin( 25 ) (Fig.”