Bayesian selection for coarse-grained models of liquid water.

ABSTRACT: The necessity for accurate and computationally efficient representations of water in atomistic simulations that can span biologically relevant timescales has born the necessity of coarse-grained (CG) modeling. Despite numerous advances, CG water models rely mostly on a-priori specified assumptions. How these assumptions affect the model accuracy, efficiency, and in particular transferability, has not been systematically investigated. Here we propose a data driven comparison and selection for CG water models through a Hierarchical Bayesian framework. We examine CG water models that differ in their level of coarse-graining, structure, and number of interaction sites. We find that the importance of electrostatic interactions for the physical system under consideration is a dominant criterion

SUBMITTER: Zavadlav J 

PROVIDER: S-EPMC6331618 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature