In this section, we present results for the performance of the CNN model revised using the expanded training dataset comprising the original dataset along with the microstructures identified from the topology optimization runs with CNN-predicted diffusivities outside the bounds of those in the originally generated dataset (as discussed in Section 2.5). Figure shows parity plots comparing diffusivity computed from kMC simulations to that predicted from the retrained CNN model. We observe excellent agreement with an MSE/MAPE of 8.978 × 10 -4 / 3.147% and 1.164 × 10 -3 / 3.632% for structures in the modified training and testing sets, respectively. We further report the model predictions for the structures discovered on repeating the topology optimization runs using SA (as described in Section 2.4) with the revised CNN model to estimate diffusivities. From the results shown in Table , we observe improved accuracy with the revised CNN model for the structures screened during the SA runs for topology optimization. Further, a Pearson's R close to one (R = 0.985) is observed between the predicted and ground truth values.