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

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.

28 pages, 10 figures

References in corpus (4)

• Domain wall creep in epitaxial ferroelectric Pb(Zr0.2Ti0.8)O3 thin films
• Variable-free exploration of stochastic models: a gene regulatory network example
• Coarse-grained dynamics of an activity bump in a neural field model
• Apparent hysteresis in a driven system with self-organized drag

Cited by in corpus (8)

• iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes
• Landmark Diffusion Maps (L-dMaps): Accelerated manifold learning out-of-sample extension
• A study of the morphology, dynamics, and folding pathways of ring polymers with supramolecular topological constraints using molecular simulation and nonlinear manifold learning
• Reconstruction of Protein Structures from Single-Molecule Time Series
• Manifold learning techniques and model reduction applied to dissipative PDEs
• Learning emergent PDEs in a learned emergent space
• On the Parameter Combinations That Matter and on Those That do Not
• Nonparametric Infinite Horizon Kullback-Leibler Stochastic Control