Based on the ion transport model considered in this study, the cation diffusivity is expected to be significantly influenced by the nanoparticle volume fraction. To that end, nanoparticles were randomly added to (removed from) each structure in the while ensuring nonoverlap between nanoparticles. Figure shows parity plots comparing the CNN-predicted and kMC-calculated diffusivities for the modified input structures. For the microstructures with 15% nanoparticle volume fraction, although the volume occupied by the nanoparticles is higher than that for the structures in the training dataset (leading to increase in the activation of the model due to the increase in the number of voxels representated as 1s in the image), excellent agreement is observed (Table ). This suggests that the convolutional filters in the multiple layers of the deep network are able to accurately identify the location of nanoparticles in the microstructues, and characterize the relevant spatial correlations between multiple nanoparticle influencing cation diffusivity. We hypothesize the overpredicted diffusivity values for structures with 5% nanoparticle volume fraction to be a result of the inability of the learned CNN filters to quantify the effect of large voids in the microstructure due to particle deletions, which are not prevalent in the training structures with 10% nanoparticle volume fraction.