To capture many-body effects in assembly simulations, the NPs, the polymer grafts, and the polymer matrix need to be explicitly modeled. This is computationally expensive, even when using coarse-grained (CG) models, as the system displays many degrees of freedom involving the grafted and matrix polymer chains, and the NPs exhibit highly sluggish dynamics due to their grafts entangling with matrix chains. Due to this limitation, simulations of large system sizes, long time scales, and large and complex assemblies are unattainable. A possible solution to this problem is to integrate out the many degrees of freedom of the polymer chains and to model polymer-grafted NPs as individual sites interacting through effective potentials that depend only on interparticle distances. In recent years, ML techniques have been a viable tool to efficiently approximate the many-body interactions in atomistic systems and have been used to speed up ab initio molecular dynamics (MD) simulations. More recently, ML techniques have also shown great promise for approximating effective interactions in colloidal systems. For instance, Campos-Villalobos et. al. used a ML approach involving Behler-Parrinello symmetry functions to develop many-body potentials for bare colloidal particles suspended in non-absorbing polymers. Boattini et. al. used a similar approach to model interactions between elastic spheres and explore their assembly in 2D. A symmetry-function-based approach was also used by Chintha et al. to approximate the two-and three-body interactions between ligand-coated NPs, though no simulations using the developed potential were conducted to validate it.