Linking microscopy to predictive coating performance via machine learning

Abstract Electron microscopy (EM) and machine learning are progressively utilized to quantify coating characteristics and performance by correlating micrographs and other features with models. Recent studies cover separate techniques, but a few integrate imaging, algorithms, and engineering targets by synthesis. Here, we examine how EM produces high-resolution images of morphology, interfaces, and defects, and how learning approaches convert these images into descriptors and models. We compare learning strategies, including unsupervised and classical image processing, deep learning with U Net for coating phase and pore segmentation, label-efficient EM methods, and tree-based models in predicting tribological response. This review also recommends using benchmark datasets with complete metadata, cross-lab validation, and hybrid training accompanied by expert evaluation. Looking forward, the goal is to guide researchers in developing predictive and prescriptive processes that accelerate coating formulation and processing. Graphical abstract