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Reverse mapping method for complex polymer systems

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We present a comprehensive approach for reverse mapping of complex polymer systems in which the connectivity is created by the simulation of chemical reactions at the coarse‐grained scale. Within the… Click to show full abstract

We present a comprehensive approach for reverse mapping of complex polymer systems in which the connectivity is created by the simulation of chemical reactions at the coarse‐grained scale. Within the work, we use a recently developed generic adaptive reverse mapping procedure that we adapt to handle the varying connectivity structure resulting from the chemical reactions. The method is independent of the coarse‐grained and fine‐grained force‐fields by design and relies only on a single control parameter. We employ the approach to reverse map four different systems: a three‐component epoxy network, a trimethylol melamine network, a hyperbranched polymer, and a polyethylene terephthalate. In the case of the epoxy network, we use two fine‐scale representations: fully atomistic and united‐atom. Whereas the trimethylol melamine network the hyperbranched polymer and the polyethylene terephthalate are reverse mapped to the fully atomistic description. After the reverse mapping, we examine the fine‐grained structure by comparing the radial distribution functions with respect to the control parameter. Moreover, in the case of the epoxy we perform tensile‐test experiments and examine the resulting Young's modulus. In all cases, we show how the properties of the reverse mapped systems depend on the control parameters. In general, we see that the results are relatively insensitive to the control parameter and the resulting atomistic systems are stable. Only for the trimethylol melamine network, we notice chemically incorrect conformations when the reverse mapping is performed too fast. We provide a remedy for this issue. © 2017 Wiley Periodicals, Inc.

Keywords: network; reverse; reverse mapping; polymer systems; complex polymer

Journal Title: Journal of Computational Chemistry
Year Published: 2018

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