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

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