To develop and train DL models that can predict core-shell UCNP photophysics and ultimately enable inverse design of UCNPs with complex heterostructures that exhibit efficient UV and blue emission (300-450 nm), we generated SUNSET: a dataset of over 30,000 multi-shell UCNP spectra calculated with a high-performance kMC simulation package (RNMC) optimized for chemical reaction networks and UCNP photophysics (Figure ). SUNSET consists of four sub-collections (SUNSET- ) that include different dopant ion combinations and surface effects (see Figure ). While each of the sub-collections provides utility for model development and testing, we focus exclusively on SUNSET-1 in our main narrative; discussion of the SUNSET- collections can be found in the Supplementary Information. We focus on SUNSET-1 because this sub-collection includes nanoparticles of variable size and a variable number of shells, which is necessary for training models that have the possibility of extrapolating to more complex heterostructures and thus facilitating impactful inverse design. Further, the nanoparticle structures in SUNSET-1 are substantially larger than in SUNSET- (given that they contain multiple layers, and each layer must be at minimum 1 nm thick in order to be synthesizable), and thus the vast majority of the computational cost of SUNSET went towards the ∼6,000 simulations in SUNSET-1. We note that individual kMC trajectories often took weeks to complete, necessitating the use of high-throughput self-checkpointing workflows. Further details of our workflow infrastructure are given in Section S1.