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Coarse-graining in simulations of multicomponent polymer systems. | LitMetric

Coarse-graining in simulations of multicomponent polymer systems.

J Chem Phys

Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA.

Published: December 2014


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Article Abstract

We investigate the mapping required between the interaction parameters of two different coarse-grained simulation models to ensure a match of the long-range structural characteristics of multicomponent polymeric system. The basis for our studies is the recent work of Morse and workers, which demonstrated the existence of a mapping between the interaction parameters of different coarse-grained simulation models which allow for a matching of the peak of the disordered state structure factor in symmetric diblock copolymers. We investigate the extensibility of their results to other polymeric systems by studying a variety of systems, including, asymmetric diblock copolymers, symmetric triblock copolymers, and diblock copolymer-solvent mixtures. By using the mapping deduced in the context of symmetric diblock copolymers, we observe excellent agreement for peak in the inverse structure between both two popular coarse grained models for all sets of polymeric melt systems investigated, thus showing that the mapping function proposed for diblock copolymer melts is transferable to other polymer melts irrespective of the blockiness or overall composition. Interestingly, for the limited parameter range of polymer-solvent systems investigated in this article, the mapping functions developed for polymer melts are shown to be equally effective in mapping the structure factor of the coarse-grained simulation models. We use our findings to propose a methodology to create ordered morphologies in simulations involving hard repulsive potentials in a computationally efficient manner. We demonstrate the outcomes of methodology by creating lamellar and cylindrical phases of diblock copolymers of long chains in the popularly used Kremer-Grest simulation model.

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http://dx.doi.org/10.1063/1.4904390DOI Listing

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