Data-driven molecular modeling with the generalized Langevin equation

•Data-driven GLE approximation balances computational cost and accuracy.•Accuracy tunable by adjusting order of memory kernel approximation.•Approximate GLE predicts non-equilibrium properties, like autocorrelation, well. The complexity of molecular dynamics simulations necessitates dimension reduct...

Full description

Saved in:
Bibliographic Details
Published in:Journal of computational physics Vol. 418; p. 109633
Main Authors: Grogan, Francesca, Lei, Huan, Li, Xiantao, Baker, Nathan A.
Format: Journal Article
Language:English
Published: Cambridge Elsevier Inc 01.10.2020
Elsevier Science Ltd
Subjects:
Approximation
Coarse-grained models
Complexity
Computational physics
Data-driven parametrization
Dimension reduction
Generalized Langevin equation
Granulation
Mathematical analysis
Molecular dynamics
Molecular dynamics
Coarse-grained models
Dimension reduction
Generalized Langevin equation
Data-driven parametrization
ISSN:0021-9991, 1090-2716
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Data-driven GLE approximation balances computational cost and accuracy.•Accuracy tunable by adjusting order of memory kernel approximation.•Approximate GLE predicts non-equilibrium properties, like autocorrelation, well. 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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0021-9991
1090-2716
DOI:10.1016/j.jcp.2020.109633