Earlier studies have utilized SA for solving structural optimization problems with discrete variables. In this article, we integrate the trained CNN model with an SA algorithm to navigate the morphological space and identify microstructures exhibiting a wide range of diffusivities. From a design perspective, one may be interested in identifying structures with high diffusivities. However, discovery of low-diffusivity microstructures is also essential for progressively improving the CNN model through their incorporation in the training process as discussed in Section 2.5. Toward these objectives, two independent SA algorithms were employed to find microstructures that maximize (and minimize) the cation diffusivities, naturally leading to the discovery of microstructures with higher (and lower) simulated diffusivities than those for structures in the originally generated dataset.