Advancing Microstructural Analysis: Practical and High-Throughput AI-Based Image Processing and Automated Data Analysis Techniques for Quantification of Structural Parameters for Multicomponent Catalyst Systems

With microstructural characterization tools now achieving nanometric and even subatomic resolution, scientists can visualize structures once thought impossible. However, mere visualization underutilizes the vast potential of imaging data. To fully harness this information, researchers are advancing image processing and data analysis techniques that enable precise quantification and automated postcharacterization analysis, unlocking deeper insights into complex material systems. As a rapidly evolving field of research, this Perspective highlights developed practical image and data analysis techniques powered by computer vision (CV) and artificial intelligence (AI) algorithms. These advanced methods offer the capability of unraveling complex material interactions in multicomponent systems, revealing intriguing structural features, and enabling precise quantification. Utilizing data from scanning electron microscopy (SEM), transmission electron microscopy (TEM), and scanning transmission electron microscopy with energy dispersive spectroscopy (STEM/EDS), these methods extract key microstructural features that are critical parameters for optimizing material performance. Tested on diverse electrochemical systems, these techniques demonstrate flexibility in their applications. Their strengths and limitations are discussed, showcasing their potential to drive automated and comprehensive materials characterization and advance the field of material science.