Advanced 3D Microstructure Generation of Solid Oxide Cell Electrodes Using Conditional Generative Adversarial Network and Validation Using Nonintuitive Topological Characteristics
Sojiro Yamatoko, Masashi Kishimoto, Szymon Buchaniec, Yuting Guo, Hiroshi Iwai · Advanced Intelligent Discovery · 2025
Solid oxide cells (SOCs) have been attracting significant interest for their potential in effective energy utilization, and optimization of their electrode microstructures is strongly demanded for enhanced performance. Data‐driven approaches for computationally generating and evaluating artificial microstructures are expected to accelerate the discovery of optimal porous structures. In this study, a machine learning model based on generative adversarial networks (GANs) is developed to generate artificial porous microstructures of SOC hydrogen electrodes while precisely controlling two key structural characteristics: volume fraction and specific surface area. The generator receives a conditional vector comprising the values of volume fractions and specific surface areas, which is concatenated with a latent vector. After training on real microstructure datasets, the model successfully generates artificial structures that are visually indistinguishable from real ones, free from unnatural artifacts, and statistically consistent with the real structures. Furthermore, persistent homology analysis is employed to assess the similarity between the real and artificial structures by uncovering hidden topological characteristics of the structures. The results demonstrate that the developed GAN model captures not only explicit statistical characteristics but also implicit, nonintuitive topological characteristics of porous electrode microstructures.