To achieve high prediction accuracy for a wide variety of morphologies in the configurational space screened during the topology optimization runs, a large training dataset containing diverse microstructures and large variation of cation diffusivities is required. However, only a relatively narrow distribution of cation diffusivities was obtained for the configurations generated using random sequential adsorption (RSA) algorithm (supporting results in SI, Section S2) which ensures nonoverlap between nanoparticles in the microstructure.