Advanced 3D Microstructure Generation of Solid Oxide Cell Electrodes Using Conditional Generative Adversarial Network and Validation Using Nonintuitive Topological Characteristics

A generative adversarial network-based machine learning model generates realistic Ni-YSZ porous microstructures with precisely controlled volume fraction and specific surface area. The model captures both statistical and hidden topological features, providing a promising pathway to accelerate the rational design and optimization of complex porous materials for solid oxide fuel cell electrodes. More details can be found in article number 2500010 by Masashi Kishimoto and co-workers.