Improving the efficiency of extended ensemble simulations: The accelerated weight histogram method

Abstract

We propose a method for efficient simulations in extended ensembles, useful, e.g., for the study of problems with complex energy landscapes and for free energy calculations. The main difficulty in such simulations is the estimation of the a priori unknown weight parameters needed to produce flat histograms. The method combines several complementary techniques, namely, a Gibbs sampler for the parameter moves, a reweighting procedure to optimize data use, and a Bayesian update allowing for systematic refinement of the free energy estimate. In a certain limit the scheme reduces to the 1/t algorithm of B.E. Belardinelli and V.D. Pereyra [Phys. Rev. E 75, 046701 (2007)]. The performance of the method is studied on the two-dimensional Ising model, where comparison with the exact free energy is possible, and on an Ising spin glass.

5 pages

References in corpus (5)

• Fast Algorithm to Calculate Density of States
• Universality in three-dimensional Ising spin glasses: A Monte Carlo study
• Wang-Landau Algorithm: a Theoretical Analysis of the Saturation of the Error
• Optimal Modification Factor and Convergence of the Wang-Landau Algorithm
• Simulation via Direct Computation of Partition Functions

Cited by in corpus (5)

• Enhanced sampling methods for molecular dynamics simulations
• Accelerated weight histogram method for exploring free energy landscapes
• Specific interactions of peripheral membrane proteins with lipids: what can molecular simulations show us?
• Riemann metric approach to optimal sampling of multidimensional free-energy landscapes
• Estimating thermodynamic expectations and free energies in expanded ensemble simulations: systematic variance reduction through conditioning