Intelligent Nanomaterial Image Characterizations – A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience

Abstract Artificial Intelligence (AI) is pivotal in advancing science, including nanomaterial studies. This review explores AI‐based image processing in nanoscience, focusing on algorithms to enhance characterization results from instruments like scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, atomic force microscopy etc. It addresses the significance of AI in nanoscience, challenges in advancing AI‐based image processing for nano material characterization, and AI's role in structural analysis, property prediction, deriving structure‐property relations, dataset augmentation, and improving model robustness. Key AI techniques such as Graph Neural Networks, adversarial training, transfer learning, generative models, attention mechanisms, and federated learning are highlighted for their contributions to nano science studies. The review concludes by outlining persisting challenges and thrust areas for future research, aiming to propel nanoscience with AI. This comprehensive analysis underscores the importance of AI‐powered image processing in nanomaterial characterization, offering valuable insights for researchers.