Figure shows the comparison between diffusivities predicted by the CNN model and those computed using the kMC simulations for the randomly chosen structures evaluated during the optimization process (as discussed in Section 2.5). It is apparent that, although these diverse microstructures with a wide range of diffusivities were not included in the training dataset, the CNN model estimates the diffusivity for such structures with reasonable accuracy. Furthermore, a Pearson's R (as a measure of linear correlation) 62 of 0.975 between the CNN-predicted and ground truth diffusivities demonstrates the ability of the trained CNN model to identify the relevant spatial features influencing diffusivity, and thereby successfully progress towards optimal microstructures by integrating with SA algorithm.