scriptors as input features. In one of the methods, we use the average nearest particle distance, reported to be a measure which correlates well with the cation diffusivity for the ion transport model considered in this study. We also explored another surrogate model with nanoparticle-nanoparticle radial distribution function as the input feature to predict cation diffusivity. Although other two-point (particle-particle) correlation functions have been employed as structural descriptors for predicting properties of composites with granular and continuous phases, we observe the radial distribution functions to be a more relevant descriptor for the microstructures in our case with uniformly sized nanoparticles (supporting results in SI, Section S6.2). Further details on the architecture of the deep artificial neural networks adopted for the above physics-inspired approaches are provided in SI, Section S6.1. The performance of such models is evaluated on the same training and testing set generated using the methodology discussed in Section 2.1.