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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

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Toward canonical ensemble distribution from self-guided Langevin dynamics simulation.

Xiongwu Wu¹, Bernard R Brooks

¹Laboratory of Computational Biology, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland 20892, USA. wuxw@nhlbi.nih.gov

The Journal of Chemical Physics

|April 12, 2011

Summary

Self-guided Langevin dynamics (SGLD) simulations accelerate conformational searching by enhancing low-frequency motion. This study quantifies SGLD

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Area of Science:

Computational chemistry and molecular dynamics simulations.
Statistical mechanics and ensemble theory.

Background:

Self-guided Langevin dynamics (SGLD) simulations enhance sampling of molecular conformations by using guiding forces based on local average momentums.
While accelerating conformational search, SGLD can perturb the canonical conformational distribution.

Purpose of the Study:

To derive a quantitative description of the conformational distribution in SGLD simulations.
To establish a method for converting between canonical and self-guided ensembles.
To enable accurate conformational sampling using SGLD.

Main Methods:

Separating molecular system properties into low-frequency and high-frequency components via local averaging.
Quantitatively describing the effect of guiding forces on conformational distribution using these components.
Developing a conversion relation between canonical and self-guided ensembles.

Main Results:

A quantitative relationship was derived to describe how SGLD guiding forces affect conformational distribution.
The study demonstrated how to obtain canonical ensemble properties and distributions from SGLD simulations.
The derived relation allows for accurate conformational sampling, not just efficient searching.

Conclusions:

SGLD simulations can be accurately used for conformational sampling by applying the derived quantitative description.
The developed method bridges the gap between efficient conformational searching and accurate statistical sampling.
This work enhances the utility of SGLD for molecular simulations.