Simulated tempering based on global balance or detailed balance conditions: Suwa–Todo, heat bath, and Metropolis algorithms. Issue 31 (15th October 2015)

• Record Type: Journal Article
• Authors: Mori, Yoshiharu; Okumura, Hisashi
• Is Part Of: Journal of computational chemistry. Volume 36:Issue 31(2015)
• Page Start: 2344
• Page End: 2349
• Publication Date: 2015-10-15
• Subjects: molecular simulation -- molecular dynamics -- generalized‐ensemble algorithm -- random walk -- correlation; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.24213
• Languages: English
• ISSNs: 0192-8651

Abstract

Abstract : Simulated tempering (ST) is a useful method to enhance sampling of molecular simulations. When ST is used, the Metropolis algorithm, which satisfies the detailed balance condition, is usually applied to calculate the transition probability. Recently, an alternative method that satisfies the global balance condition instead of the detailed balance condition has been proposed by Suwa and Todo. In this study, ST method with the Suwa–Todo algorithm is proposed. Molecular dynamics simulations with ST are performed with three algorithms (the Metropolis, heat bath, and Suwa–Todo algorithms) to calculate the transition probability. Among the three algorithms, the Suwa–Todo algorithm yields the highest acceptance ratio and the shortest autocorrelation time. These suggest that sampling by a ST simulation with the Suwa–Todo algorithm is most efficient. In addition, because the acceptance ratio of the Suwa–Todo algorithm is higher than that of the Metropolis algorithm, the number of temperature states can be reduced by 25% for the Suwa–Todo algorithm when compared with the Metropolis algorithm. © 2015 Wiley Periodicals, Inc. Abstract : The autocorrelation of the potential energy depends on algorithms that are used when the transition probability of simulated tempering is calculated. The reduction of the autocorrelation time is important for efficient sampling. The Suwa–Todo algorithm shows better sampling efficiency than the Metropolis algorithm, which is usually used in Monte Carlo simulations.