From generalized Langevin equations to Brownian dynamics and embedded Brownian dynamics

Lina Ma, Xiantao Li, Chun Liu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


We present the reduction of generalized Langevin equations to a coordinate-only stochastic model, which in its exact form involves a forcing term with memory and a general Gaussian noise. It will be shown that a similar fluctuation-dissipation theorem still holds at this level. We study the approximation by the typical Brownian dynamics as a first approximation. Our numerical test indicates how the intrinsic frequency of the kernel function influences the accuracy of this approximation. In the case when such an approximate is inadequate, further approximations can be derived by embedding the nonlocal model into an extended dynamics without memory. By imposing noises in the auxiliary variables, we show how the second fluctuation-dissipation theorem is still exactly satisfied.

Original languageEnglish (US)
Article number114102
JournalJournal of Chemical Physics
Issue number11
StatePublished - Sep 21 2016

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy
  • Physical and Theoretical Chemistry


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