Seeing the Middle: Reconstructing 3D Internal Electrode Microstructures from Low‐Resolution Surfaces with Generative Diffusion Artificial Intelligence

Characterizing the 3D complex energy materials interface is critical to understand the correlative relationship between performance, degradation, and structures. Unfortunately, the resolution of microscopy and image acquisition speed are limited by the nature of the hardware, causing high-throughput characterization of energy materials to be prohibitive. Herein, REMind, a generative diffusion artificial intelligence model for fast and accurate reconstruction of electrode microstructures via focused ion beam-scanning electron microscopy, is presented. REMind can generate high-resolution internal microstructures between two low-resolution surfaces after training on sufficient high-resolution microstructures, enabling larger milling thickness between slices while keeping high-fidelity imaging. REMind is first demonstrated for reconstructing solid oxide fuel cell (SOFC) anode microstructures. REMind resolves relevant multi-scale structures with low pixel-wise reconstruction error (<10%) and quantifies the generated uncertainty by calculating the generated entropy. Additionally, a multi-scale multi-physics SOFC model is employed to further quantify the reconstructed error regarding the electrochemical performance, i.e., operating current density versus overpotential. REMind shows good transferability, as proven by its ability to reconstruct other energy materials, including catalyst layers of proton exchange membrane fuel cells and solid-state battery composite electrodes, demonstrating the potential for REMind to be used as a general-purpose platform for broad development of energy technology.