Theory of Wavelet-Based Coarse-Graining Hierarchies for Molecular Dynamics

Abstract

We present a multiresolution approach to compressing the degrees of freedom (DoFs) and potentials associated with molecular dynamics (MD). We suggest a systematic way to accelerate large-scale MD with more than 2 levels of coarse-graining, particularly for simulation of polymeric materials. We derive explicit models for linear polymers and iterative methods to compute large-scale wavelet decompositions from fragment solutions. This approach does not require explicit preparation of atomistic-to-coarse-grained (CG) mappings, but instead uses diffusion wavelets for graph Laplacians to develop system-specific mappings. Our methodology leads to a hierarchy of system-specific CG DoFs that provide a conceptually clear and rigorous framework for modeling chemical systems at relevant model scales. The approach is capable of automatically generating as many CG model scales as necessary, that is, to go beyond the 2 scales in conventional CG strategies. Furthermore, the wavelet-based CG models explicitly link time and length scales. Finally, a straightforward method to introduce omitted DoFs is presented, which plays a major role in maintaining model fidelity in long-time simulations and capturing emergent behaviors.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2017
Accession Number
AD1033178

Entities

People

  • Ahmed E. Ismail
  • Berend C. Rinderspacher
  • Jaydeep P. Bardhan

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Cartesian Coordinates
  • Coefficients
  • Computational Science
  • Construction
  • Coordinate Systems
  • Decomposition
  • Dynamics
  • Eigenvalues
  • Eigenvectors
  • Equations
  • Frequency
  • Materials
  • Military Research
  • Molecular Dynamics
  • Molecular Dynamics Simulations
  • Molecular Mechanics Methods
  • Molecules
  • Phase Transformations
  • Polymers
  • Power Spectra
  • Probability
  • Probability Distributions
  • Simulations
  • Vector Spaces
  • Wavelet Transforms

Readers

  • Computational Fluid Dynamics (CFD)
  • Graph Algorithms and Convex Optimization.