From Top to Bottom: Manufacturing Process‐Context Aware Resolution of Energy Device Electrodes Through a 3D Diffusion Generative Model

Abstract

Optimization of electrochemical energy storage and conversion devices often depends on the electrode microstructure properties, of which its 3D representation provides the most complete link between manufacturing process and device performance. Deep generative models can address slow experimental throughput and high computational costs of traditional microstructure resolution methods, yet most literature focuses on Generative Adversarial Networks, prone to mode collapse issues. This work applies a generative diffusion model to synthesize 3D‐resolved multiphase microstructures of lithium ion battery and proton exchange membrane fuel cell electrodes, sourced from experimental imaging and computer‐based simulations. Generated volumes are validated against training data based on microstructural descriptors, including volume fraction, specific surface area, and tortuosity factor. Moreover, the standard cubic sub‐volume augmentation strategy used forgenerative model training fails to capture depth‐dependent features in thick and heavily processed microstructures, such as calendered electrodes. A depth‐aware augmentation pipeline is proposed to incorporate full electrode thickness into training samples, improving through‐thickness tortuosity factor accuracy in the generated electrodes. Furthermore, 3D‐resolved multiphysics simulation on the generated microstructures reproduce the performance behavior of their training counterparts with high accuracy. These results establish a diffusion model‐based framework as a viable means to generate commercially relevant electrode data, intended for computational optimization and design of next‐generation energy devices.