  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.
