Wavelet-accelerated Monte Carlo sampling of polymer chains

Ahmed E. Ismail, George Stephanopoulos, Gregory C. Rutledge

Research output: Contribution to journalArticle

Abstract

We introduce a new method for coarse-graining polymer chains, based on the wavelet transform, a multiresolution data analysis technique. This method, which assigns a cluster of particles to a coarse-grained bead located at the center of mass of the cluster, reduces the complexity of the problem significantly by dividing the simulation into several stages, each with a small fraction of the number of beads in the overall chain. At each stage, we compute the distributions of coarse-grained internal coordinates as well as potential functions required for subsequent simulation stages. We show that, with this wavelet-accelerated Monte Carlo method, coarse-grained Gaussian and self-avoiding random walks can reproduce results obtained from atomistic simulations to a high degree of accuracy in orders of magnitude less time.

Original languageEnglish (US)
Pages (from-to)897-910
Number of pages14
JournalJournal of Polymer Science, Part B: Polymer Physics
Volume43
Issue number8
DOIs
StatePublished - Apr 15 2005
Externally publishedYes

Fingerprint

Wavelet transforms
Polymers
Monte Carlo methods
sampling
Sampling
beads
polymers
simulation
random walk
wavelet analysis
center of mass
Monte Carlo method

Keywords

  • Coarse-graining
  • Lattice models
  • Molecular modelling
  • Monte Carlo simulation

ASJC Scopus subject areas

  • Polymers and Plastics
  • Materials Chemistry

Cite this

Wavelet-accelerated Monte Carlo sampling of polymer chains. / Ismail, Ahmed E.; Stephanopoulos, George; Rutledge, Gregory C.

In: Journal of Polymer Science, Part B: Polymer Physics, Vol. 43, No. 8, 15.04.2005, p. 897-910.

Research output: Contribution to journalArticle

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