Data-driven molecular modeling with the generalized Langevin equation

JOURNAL OF COMPUTATIONAL PHYSICS

卷:418

Article

Grogan, Francesca¹  Lei, Huan^2,3  Li, Xiantao⁴  Baker, Nathan A.^1,5 

[1] Pacific Northwest Natl Lab, Richland, WA 99352 USA

[2] Michigan State Univ, Dept Computat Math Sci & Engn, E Lansing, MI 48824 USA

[3] Michigan State Univ, Dept Stat & Probabil, E Lansing, MI 48824 USA

[4] Penn State Univ, Dept Math, State Coll, PA 16801 USA

[5] Brown Univ, Div Appl Math, Providence, RI 02912 USA

关键词: Molecular dynamics;    Generalized Langevin equation;    Coarse-grained models;    Dimension reduction;    Data-driven parametrization;   

DOI  :  10.1016/j.jcp.2020.109633

来源: Elsevier

【 摘 要 】

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. (C) 2020 Elsevier Inc. All rights reserved.

【 授权许可】

Free