In the absence of a preexisting analytical model for predicting transport properties in nanoparticle-based composites (for which nanoparticle-surface transport pathways have a strong influence), artificial neural network models were trained with simple, structural de-Figure : Schematic of the alternative physics-inspired deep-learning models using (a) average nearest particle distance or (b) particle-particle radial distribution function as microstructural inputs to predict cation diffusivity.