AI‐Powered Visualization of Invisible Mechano‐Information: Stress, Defects, and Beyond

Abstract Despite significant advances in high‐resolution structural characterization, visualizing complex mechano‐information—such as local stress fields induced by lattice distortions or elemental distributions—remains a formidable challenge. This “invisible” information, inaccessible through current experimental techniques, hinders a comprehensive understanding of material properties and behaviors across multiple fields. Artificial intelligence (AI) has emerged as a transformative tool, bridging material properties with their structures and enabling the visualization of previously hidden mechano‐information. This review explores AI‐driven approaches to reveal mechano‐information, including local stress distributions across scales (from macroscale to nanoscale) and the distribution of ultra‐light elements at lattice defects, along with their effects on local stress fields. Additionally, recent AI‐assisted methods for visualizing structural, chemical, and functional information are highlighted, and current challenges and future opportunities in this rapidly evolving field are discussed.