To explore the utility of our differentiable model for inverse design, we use the hetero-GNN trained on SUNSET-1 with augmented data to search for Yb/Er/Ndcodoped UCNPs with the highest UV/blue intensities under 800-nm excitation. To facilitate the discovery of novel heterostructures, we conduct optimizations far beyond the structural distribution spanned by our training data. While the SUNSET-1 training set contains UCNP structures with up to four regions (i.e., a core and three shells) and with a maximum radius of 11.5 nm, our optimizations explore UCNPs with up to ten regions (i.e., a core and nine shells) and a maximum radius of 15 nm. Further, while the core radius is limited to a maximum of 4 nm and shells are limited to a maximum thickness of 2.5 nm in the training data, we remove both of these upper bounds and only limit the overall radius during optimization. However, we do retain a minimum core radius and shell thickness of 1 nm to ensure that the optimized heterostructures are synthesizeable . Given randomly initialized nanoparticle structures, we employ a combination of trust region constrained local optimization and basin hopping global optimization to identify UCNPs with maximized UV/blue emission as a function of maximum allowed nanoparticle radius and the number of distrinct regions in the heterostructure. Section 5 contains additional details of our optimization approach. The maximum intensities identified for UCNPs of different sizes and different numbers of regions (core + shells) are illustrated in Figure . The optimal structure for each distinct radius + region number was then simulated with kMC, for which larger radius particles often required months-long simulations. The colors of the two triangles in each square indicate the kMC-simulated and ML-predicted intensities for the optimized structure. Remarkably, the model demonstrates accurate predictions for the upconversion luminescence of particles with significantly out-of-distribution radii and numbers of regions, including when the UV emissions approach an order of magnitude higher than those in the training set.