Self-guided Langevin dynamics via generalized Langevin equation

Xiongwu Wu, Bernard R. Brooks, Eric Vanden Eijnden

Research output: Contribution to journalArticle

Abstract

Self-guided Langevin dynamics (SGLD) is a molecular simulation method that enhances conformational search and sampling via acceleration of the low frequency motions of the system. This acceleration is produced via introduction of a guiding force which breaks down the detailed-balance property of the dynamics, implying that some reweighting is necessary to perform equilibrium sampling. Here, we eliminate the need of reweighing and show that the NVT and NPT ensembles are sampled exactly by a new version of self-guided motion involving a generalized Langevin equation (GLE) in which the random force is modified so as to restore detailed-balance. Through the examples of alanine dipeptide and argon liquid, we show that this SGLD-GLE method has enhanced conformational sampling capabilities compared with regular Langevin dynamics (LD) while being of comparable computational complexity. In particular, SGLD-GLE is fully size extensive and can be used in arbitrarily large systems, making it an appealing alternative to LD.

Original languageEnglish (US)
Pages (from-to)595-601
Number of pages7
JournalJournal of Computational Chemistry
Volume37
Issue number6
DOIs
StatePublished - Mar 5 2016

Fingerprint

Generalized Langevin Equation
Langevin Dynamics
Detailed Balance
Sampling
Molecular Simulation
Motion
Argon
Dipeptides
Simulation Methods
Breakdown
Low Frequency
Computational Complexity
Ensemble
Alanine
Eliminate
Liquid
Computational complexity
Necessary
Alternatives
Liquids

Keywords

  • canonical ensemble
  • conformational sampling
  • generalized Langevin equation
  • molecular simulation
  • self-guided Langevin dynamics

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics

Cite this

Self-guided Langevin dynamics via generalized Langevin equation. / Wu, Xiongwu; Brooks, Bernard R.; Vanden Eijnden, Eric.

In: Journal of Computational Chemistry, Vol. 37, No. 6, 05.03.2016, p. 595-601.

Research output: Contribution to journalArticle

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