Many-body interactions between polymer-grafted nanoparticles (NPs) play a key role in promoting their assembly into low-dimensional structures within polymer melts, even when the particles are spherical and isotropically grafted. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. Here, we develop a many- body potential for describing the effective interactions between spherical polymer-grafted NPs in a polymer matrix through a machine learning (ML) approach. The approach involves using permutationally invariant polynomials to fit two- and three-body interactions derived from potential of mean force (PMF) calculations. This potential developed here reduces the computational cost by several orders of magnitude, thereby allowing us to explore assembly behavior over large length and time scales. We show that the potential not only reproduces previously known assembled phases, such as 1D strings and 2D hexagonal sheets, which cannot be achieved using two-body potentials, but can also help discover interesting new phases, such as networks, clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition between two- and three-body interactions. Our approach for deriving many- body effective potentials can be readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior and dissect the role of individual interaction energy terms in the overall potential in the observed behavior.
additional figures and tables showing parity plots, three-body contributions, energy landscapes, optimized parameters, radial distribution functions, assembly morphologies