To train these DL models, we generated a dataset of Simulated Upconverting Nanoparticle Spectra for Emission Tuning (SUNSET), consisting of results from ∼6,000 kinetic Monte Carlo (kMC) simulations of nanoparticle photophysics (Figure ). Models trained on SUNSET aim to predict photon emission within a specified wavelength band as a function of UCNP heterostructure. By training on simulations of UCNPs with up to three shells and evaluating on simulations with four shells, we can quantify the capacity for models to extrapolate to larger and more complex heterostructures. We find that our heterogeneous graph representation, informed by UCNP physics and geometry, allows DL models to achieve far higher prediction accuracy than tabular, image, and homogeneous graph representations (Figure ), especially when extrapolating beyond the training data. The differentiability of our heterogeneous GNN also yields gradients of emission intensity with respect to layer thicknesses and dopant concentrations (Figure ), which are not accessible from kMC. Our trained model thus facilitates inverse design of UCNP heterostructure via gradient-based optimization (Figure ), identifying novel superior UCNPs with a range of sizes and up to ten shells. When excited at 980 or 800 nm, these optimized UCNP heterostructures exhibit exceptionally high emission between 300-450 nm, a spectral range useful for inducing photochemistry for optogenetic, catalytic, therapeutic, and 3D printing applications. To validate these predictions, we perform additional months-long kMC simulations which indicate that our model possesses considerable ability to extrapolate far out of distribution and can suggest never-before-seen structures with high accuracy, further revealing novel design principles. These findings demonstrate a path forward for the optimization and discovery of technologically useful UCNPs and offer inspiration for the development of novel DL representations and models which enable inverse design for a broad range of optical nanomaterials.