Data-driven molecular modeling with the generalized Langevin equation

Francesca Grogan, Huan Lei, Xiantao Li, Nathan A. Baker

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve. To address these issues, we construct a data-driven rational approximation to the GLE. Building upon previous work leveraging the GLE to simulate simple systems, we extend these results to more complex molecules, whose many degrees of freedom and complicated dynamics require approximation methods. We demonstrate the effectiveness of our approximation by testing it against exact methods and comparing observables such as autocorrelation and transition rates.

Original languageEnglish (US)
Article number109633
JournalJournal of Computational Physics
StatePublished - Oct 1 2020

All Science Journal Classification (ASJC) codes

  • Numerical Analysis
  • Modeling and Simulation
  • Physics and Astronomy (miscellaneous)
  • General Physics and Astronomy
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics


Dive into the research topics of 'Data-driven molecular modeling with the generalized Langevin equation'. Together they form a unique fingerprint.

Cite this