Deriving effective mesoscale potentials from atomistic simulations

TL;DR

This paper presents an iterative method to derive effective mesoscale potentials from atomistic simulations, successfully applied to coarse-grain poly(isoprene) systems and highlighting the variability of force fields across different concentrations.

Contribution

It generalizes a potential inversion method to polymer systems and demonstrates its effectiveness in coarse-graining poly(isoprene) from atomistic data.

Findings

Convergence achieved in few iterations for potential inversion.

Different force fields are required for different concentration regimes.

Method successfully applied to coarse-grain poly(isoprene) systems.

Abstract

We demonstrate how an iterative method for potential inversion from distribution functions developed for simple liquid systems can be generalized to polymer systems. It uses the differences in the potentials of mean force between the distribution functions generated from a guessed potential and the true (simulated) distribution functions to improve the effective potential successively. The optimization algorithm is very powerful: convergence is reached for every trial function in few iterations. As an extensive test case we coarse-grained an atomistic all-atom model of poly (isoprene) (PI) using a 13:1 reduction of the degrees of freedom. This procedure was performed for PI solutions as well as for a PI melt. Comparisons of the obtained force fields are drawn. They prove that it is not possible to use a single force field for different concentration regimes.