From the results displayed in Figure , the CNN model is able to furnish more accurate predictions than the physics-inspired models. This suggests that the physics-based descriptors, which do not contain information pertaining to many-body spatial correlations, may be insufficient for identifying the most relevant microstructural features influencing cation diffusivity. For the CNN model, although the convolutional filter size is smaller than the size of a nanoparticle, the spatial correlations between multiple nanoparticles can be captured by the deeper network implemented in this study with four convolutional layers. More The quantitative agreement between the CNN predictions and the ground truth diffusivities provides evidence that CNNs are a promising tool for predicting macroscopic properties of two-phase composites comprising spherical nanoparticles. Further, since predicting diffusivity using the CNN model is faster than running brute kMC simulations, it can be an efficient proxy in frameworks that require many evaluations of cation diffusivity. We therefore integrate the CNN models with the topology optimization algorithms which requires calculating cation diffusivity at each step, to help discover new composite materials with desired characteristics and develop even more accurate predictive tools as discussed below.