We discuss an information-theoretic approach to coarse-graining based on the relative entropy.  This quantity measures the information lost upon coarse graining and hence the (inverse) fitness of a particular coarse-grained model.  Minimization of the relative entropy thus provides a variational principle for coarse-graining, and a way to �automatically� discover and generate coarse models of many systems.  We discuss conceptual and numerical aspects of this approach and illustrate its use with several case studies, including water, hydrophobic interactions, peptide folding, and peptide self-assembly.  We also discuss extensions of the relative entropy approach to address transferability of coarse-grained potentials, multibody interaction effects, and the design of coarse-grained models in addition to potentials.

Relative entropy approach to coarse-graining

248th National Meeting (2014)

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