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

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.