BAR-based optimum adaptive sampling regime for variance minimization in alchemical transformation

Literature Information

Publication Date 2017-05-15
DOI 10.1039/C7CP01561E
Impact Factor 3.676
Authors

Zhao X. Sun, Xiao H. Wang


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Abstract

The efficiency of alchemical free energy simulations with the staging strategy is improved by adaptively manipulating the significance of each ensemble followed by importance sampling. The OBAR (optimum BAR) method introduced in this work with explicit consideration of the statistical inefficiency in each ensemble outperforms the traditional equal time rule which is used in standard applications of alchemical transformation with the window sampling regime in the sense of minimizing the total variance of the free energy estimate. The Time Derivative of total Variance (TDV) is proposed for the OBAR criterion which is linearly dependent on the variance and is more sensitive to the importance rank than the overlap matrix. The performance of OBAR workflow is demonstrated for solvation of several small molecules and a protein ligand binding system.

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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