Coarse-graining the Dynamics of a Driven Interface in the Presence of Mobile Impurities: Effective Description via Diffusion Maps

TL;DR

This paper employs diffusion maps, a data mining technique, to identify effective coarse variables for describing the dynamics of driven interfaces with mobile impurities, improving simulation efficiency and understanding.

Contribution

It introduces a diffusion map-based method for automatic coarse variable selection, enhancing previous empirical approaches in modeling driven interfaces with impurities.

Findings

Diffusion maps effectively identify relevant coarse variables.

The approach improves simulation efficiency of interface dynamics.

Empirical and automatic coarse variables show good correspondence.

Abstract

Developing effective descriptions of the microscopic dynamics of many physical phenomena can both dramatically enhance their computational exploration and lead to a more fundamental understanding of the underlying physics. Previously, an effective description of a driven interface in the presence of mobile impurities, based on an Ising variant model and a single empirical coarse variable, was partially successful; yet it underlined the necessity of selecting additional coarse variables in certain parameter regimes. In this paper we use a data mining approach to help identify the coarse variables required. We discuss the implementation of this diffusion map approach, the selection of a similarity measure between system snapshots required in the approach, and the correspondence between empirically selected and automatically detected coarse variables. We conclude by illustrating the use of the diffusion map variables in assisting the atomistic simulations, and we discuss the translation of information between fine and coarse descriptions using lifting and restriction operators.