Inverse Design of Complex Nanoparticle Heterostructures via Deep Learning on Heterogeneous Graphs

Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. We report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core-shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in e.g., biosensing, super-resolution microscopy, and 3D printing, can emit visible and ultraviolet light from near-infrared excitations. We report the first large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use this data to train a heterogeneous graph neural network (GNN) using a novel representation of UCNP nanostructure. Applying gradient-based optimization on the trained GNN, we identify structures with 6.5 times higher predicted emission under 800nm illumination than any UCNP in our training set. Our work reveals new design principles for UCNPs and presents a roadmap for DL-based inverse design of nanomaterials.