Coarse-graining using the relative entropy and simplex-based optimization methods in VOTCA

ORAL

Abstract

Coarse-grained (CG) simulations are an important tool to investigate systems on larger time and length scales. Several methods for systematic coarse-graining were developed, varying in complexity and the property of interest. Thus, the question arises which method best suits a specific class of system and desired application. The Versatile Object-oriented Toolkit for Coarse-graining Applications (VOTCA) provides a uniform platform for coarse-graining methods and allows for their direct comparison. We present recent advances of VOTCA, namely the implementation of the relative entropy method and downhill simplex optimization for coarse-graining. The methods are illustrated by coarse-graining SPC/E bulk water and a water-methanol mixture. Both CG models reproduce the pair distributions accurately.

*SYM is supported by AFOSR under grant 11157642 and by NSF under grant 1264282. CJ was supported in part by the NSF PHY11-25915 at KITP. K. Koschke acknowledges funding by the Nestle Research Center.

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Authors

  • Victor R\"uhle

    • Department of Chemistry, University of Cambridge, Cambridge

  • Mara Jochum

    • MPI-P, Mainz, Germany

  • Konstantin Koschke

    • MPI-P, Mainz, Germany

  • N.R. Aluru

    • Beckman Institute for Advanced Science and Technology, Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
    • Department of Mechanical Science and Engineering, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign

  • Kurt Kremer

    • Max-Planck Institute for Polymer Research
    • MPI-P, Mainz, Germany

  • S.Y. Mashayak

    • Beckman Institute for Advanced Science and Technology, Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
    • Department of Mechanical Science and Engineering, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign

  • Christoph Junghans

    • Theory Division, LANL, Los Alamos, NM, USA