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Wavelet-accelerated Monte Carlo sampling of polymer chains

Authors

  • Ahmed E. Ismail,

    1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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  • George Stephanopoulos,

    1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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  • Gregory C. Rutledge

    Corresponding author
    1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
    • Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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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. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 43: 897–910, 2005

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