Coarse-Grained Dynamics with Spatial Disorder and Non-Markovian Memory

TL;DR

The paper presents SD-GLE, a data-driven coarse-grained modeling approach that explicitly accounts for spatial disorder and non-Markovian effects, improving long-time dynamics prediction in heterogeneous systems.

Contribution

It introduces SD-GLE, a novel variational Bayesian framework that disentangles static spatial disorder from viscoelastic friction in coarse-grained dynamics.

Findings

SD-GLE accurately captures long-time dynamics and anomalous diffusion crossover.

Standard GLEs have limitations in disordered systems, which SD-GLE overcomes.

Numerical results validate SD-GLE's ability to recover ensemble statistical properties.

Abstract

We introduce the spatial disorder-generalized Langevin equation (SD-GLE), a data-driven method for constructing coarse-grained (CG) dynamics in heterogeneous systems. Unlike conventional CG approaches that rely on a mean-field potential, SD-GLE utilizes a variational Bayesian framework with a random field prior to explicitly disentangle static spatial disorder from viscoelastic friction. Numerical results demonstrate the limits of standard GLEs, whereas SD-GLE accurately extrapolates long-time dynamics to capture the anomalous diffusion crossover from short trajectories and recover the ensemble statistical properties inherent to the disordered nature of these systems.