Despite the ubiquity of nanoparticles in modern materials research, computational scientists are often forced to choose between simulations featuring detailed models of only a few nanoparticles or simplified models with many nanoparticles. Herein, we present a coarse-grained model for amorphous silica nanoparticles with parameters derived via potential matching to atomistic nanoparticle data, thus enabling large-scale simulations of realistic models of silica nanoparticles. Interaction parameters are optimized to match a range of nanoparticle diameters in order to increase transferability with nanoparticle size. Analytical functions are determined such that interaction parameters can be obtained for nanoparticles with arbitrary coarse-grained fidelity. The procedure is shown to be extensible to the derivation of cross-interaction parameters between coarse-grained nanoparticles and other moieties and validated for systems of grafted nanoparticles. The optimization procedure used is available as an open-source Python package and should be readily extensible to models of non-silica nanoparticles.