Due to the limited microstructural domains featured in the dataset generated using the methodology discussed in Section 2.1, augmenting the microstructures with those identified from optimizing the CNN model with the SA algorithm, and subsequently retraining the CNN model with the expanded dataset, is expected to improve diffusivity predictions for a wider range of nanoparticle morphologies. To this end, cation diffusivities for the structures identified from the topology optimization runs were calculated using our kMC simulation methodology. Specifically, from the structures evaluated during the SA algorithm for maximizing diffusivity, 5000 configurations with uniform distribution of CNN-predicted diffusivities higher than the maximum diffusivity value in the original dataset were chosen randomly, analyzed using the kMC simulations and added to the dataset. Similarly, 2280 structures from the ones explored using the SA algorithm for minimizing diffusivity were evaluated using kMC simulation and appended to the dataset as well. The numbers of training, validation and test data points for the new dataset were 9712, 3240, and 3240, respectively. Following the retraining of the CNN model, the topology optimization process described in Section 2.4 was repeated using the updated CNN model.