Resolving Dynamic Properties of Polymers through Coarse-Grained Computational Studies

TL;DR

This study investigates how different levels of coarse graining in computational models affect the ability to accurately simulate the long-time dynamics and viscoelastic properties of polymers, specifically polyethylene.

Contribution

It introduces a systematic approach to determine the optimal coarse-graining scale that retains atomistic details while enabling large-scale, long-time simulations of polymer dynamics.

Findings

Atomistic detail is essential for accurate large-scale dynamics.

Coarse-grained models can simulate over 500 μs of polymer melt behavior.

Optimal coarse-graining balances detail retention with computational efficiency.

Abstract

Coupled length and time scales determine the dynamic behavior of polymers and underlie their unique viscoelastic properties. To resolve the long-time dynamics it is imperative to determine which time and length scales must be correctly modeled. Here we probe the degree of coarse graining required to simultaneously retain significant atomistic details and access large length and time scales. The degree of coarse graining in turn sets the minimum length scale instrumental in defining polymer properties and dynamics. Using linear polyethylene as a model system, we probe how coarse graining scale affects the measured dynamics. Iterative Boltzmann inversion is used to derive coarse-grained potentials with 2-6 methylene groups per coarse-grained bead from a fully atomistic melt simulation. We show that atomistic detail is critical to capturing large scale dynamics. Using these models we simulate polyethylene melts for times over 500 {\mu}s to study the viscoelastic properties of well-entangled polymer chains.