Multiblob coarse-graining for mixtures of long polymers and soft colloids

TL;DR

This paper introduces a novel coarse-graining method for simulating mixtures of long polymers and soft colloids, significantly reducing computational complexity while maintaining quantitative accuracy.

Contribution

The authors develop a transferable potential-based coarse-graining approach for polymer-colloid mixtures, enabling efficient and accurate modeling of complex nanocomposites.

Findings

The method accurately reproduces properties of polymer-colloid mixtures.

It allows for substantial reduction of degrees of freedom in simulations.

Validated against full monomer simulations across different molecular weights.

Abstract

Soft nanocomposites represent both a theoretical and an experimental challenge due to the high number of the microscopic constituents that strongly influence the behaviour of the systems. An effective theoretical description of such systems invokes a reduction of the degrees of freedom to be analysed, hence requiring the introduction of an efficient, quantitative, coarse-grained description. We here report on a novel coarse graining approach based on a set of transferable potentials that quantitatively reproduces properties of mixtures of linear and star-shaped homopolymeric nanocomposites. By renormalizing groups of monomers into a single effective potential between a $f$-functional star polymer and an homopolymer of length $N_0$, and through a scaling argument, it will be shown how a substantial reduction of the to degrees of freedom allows for a full quantitative description of the system. Our methodology is tested upon full monomer simulations for systems of different molecular weight, proving its full predictive potential.