Figure illustrates the workflow implemented in this study. In this section, we discuss the methodologies adopted to generate the training dataset, train the CNN-based deep learning The ground truth data used in the training process is generated via a multiscale simulation framework which computes the cation diffusivities for a large dataset of diverse microstructures at a fixed nanoparticle loading. Section 2.1 describes the physics-inspired strategy employed to generate the dataset of diverse microstructures at a fixed nanoparticle loading to span a wide range of cation diffusivities for training the machine learning models. Section 2.2 discusses the methodology to compute ground truth cation diffusivity for a given nanoparticle microstructure. The details pertaining to the CNN architecture and the topology optimization runs are presented in Section 2.3 and Section 2.4, respectively. An improved predictive model is developed by appending the structures identified using the optimization strategy (as well as the corresponding simulated cation diffusivities) to the initial training dataset, and subsequently retraining the CNN model as discussed in Section 2.5.