While we have here focused on UCNPs, we believe that the heterogeneous graph representation that we have described and implemented could be suitable to predict heterostructure-dependent properties in other multi-layered nanomaterials. Possible applications include engineering the nanophotonic properties of plasmonic and dielectric nanoparticles, the catalytic properties of polyelemental heterostructures, the optoelectronic properties of complex semiconductor nanoparticle heterostructures, the layer-by-layer assembly of nanoparticles for drug delivery, multilayered magnetic nanospheres, and multilayer graphene sheets for diverse energy and mechanical applications. We also note that, given a DL model which can predict multiple properties controlled by heterostructure, our approach could allow for structural optimization while maximizing or minimizing multiple properties simultaneously.