Systematic coarse graining: "Four lessons and a caveat" from nonequilibrium statistical mechanics

TL;DR

This paper discusses how nonequilibrium statistical thermodynamics can guide systematic coarse graining in simulations, improving the extraction of consistent and non-redundant information from dynamic systems, exemplified by polyethylene melt simulations.

Contribution

It introduces a thermodynamics-guided approach to coarse graining, emphasizing systematic methods over brute-force simulations, with practical examples.

Findings

Monte Carlo, molecular dynamics, and Brownian dynamics are effective in thermodynamic coarse graining.

The approach yields complete and redundancy-free information.

Application to polyethylene melt demonstrates practical utility.

Abstract

With the guidance offered by nonequilibrium statistical thermodynamics, simulation techniques are elevated from brute-force computer experiments to systematic tools for extracting complete, redundancy-free and consistent coarse grained information for dynamic systems. We sketch the role and potential of Monte Carlo, molecular dynamics and Brownian dynamics simulations in the thermodynamic approach to coarse graining. A melt of entangled linear polyethylene molecules serves us as an illustrative example.