A Systematic Coarse-graining Approach for High-molecular Weight Polymers

A major challenge in the study of both natural and synthetic polymer systems is the theory-based design of polymers with desired properties. While computational statistical mechanics provides a framework to establish structure-property relationships that facilitate design, many important phenomena require the study of time- and length scales that are beyond the capabilities of atomistic molecular dynamics and thus require different simulation strategies. In this talk, we discuss a systematic coarse-graining approach that enables study of systems at targeted resolution. The approach, which utilizes basic concepts from graph theory, provides an unambiguous and automated way to generate coarse-grained representations that preserve chemical topology and are thus sensitive to chemical substitutions. We highlight this approach in application to several high-molecular weight polysaccharides and examinethe properties for several polysaccharides at different degrees of resolution. We further assess the performance of both traditional and machine-learning parameterization schemes. The methodology described provides a framework that should be useful for coarse-grained study of various soft matter systems.