426246 Systematic and Simulation-Free Coarse Graining of Multi-Component Polymeric Systems

Thursday, November 12, 2015: 1:15 PM
251B (Salt Palace Convention Center)
Delian Yang and Qiang (David) Wang, Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO

Coarse-grained (CG) models are currently needed to study polymeric systems, as full atomistic simulations of many-chain systems used in experiments are in most cases not feasible due to their formidable computational requirements. Polymeric systems are also best suited for coarse graining, as the large number of monomers on each chain allows high levels of coarse graining. Here we propose a systematic and simulation-free strategy for coarse graining multi-component polymeric systems (e.g., polymer solutions, blends, nanocomposites, etc.), where we use the well-developed polymer reference interaction site model theory, instead of many-chain molecular simulations, for both the original and CG systems, and examine how the CG potentials vary with N (the number of CG segments on each chain) and how well the CG models can reproduce the structural and thermodynamic properties of the original system. Our strategy is quite general and versatile, and can be applied to both structure- and relative-entropy-based coarse graining. It is at least several orders of magnitude faster than those using many-chain simulations, thus effectively solving the transferability problem in coarse graining, and provides a quantitative basis for choosing small N-values that can still capture the chain conformational entropy, a characteristics of polymers. It also avoids the problems caused by finite-size effects and statistical uncertainties in many-chain simulations commonly used in coarse graining.

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