with Machine learning approach for deriving analytical many-body potentials for modeling the effective interactions between polymer-grafted NPs in a polymer matrix. The approach involves training permutationally invariant polynomials to two-and three-particle PMFs collected from MD simulations. The resulting potential can then be used to investigate assembly behavior of NPs for orders of magnitude larger length and time scales than achievable through explicit description of the polymer chains.