Artificial intelligence-enabled materials innovation: Implementation levels and strategic layout

Materials innovation has long been constrained by vast design spaces, complex processing routes, lengthy validation cycles, and difficulties in engineering translation. Traditional research and development models, which mainly rely on accumulated experience, theoretical deduction, and experimental trial and error, have become increasingly insufficient to meet the demand for rapid breakthroughs in critical materials. In recent years, artificial intelligence has been increasingly integrated into materials design, synthesis, and processing, characterization, evaluation, optimization, and application feedback, promoting the transformation of materials innovation from experience-driven exploration to data-driven development and from discrete trial and error to closed-loop optimization. Based on an analysis of the evolution of materials science research paradigms and technological innovation paradigms, this study proposes a three-level implementation framework for artificial intelligence-enabled materials innovation, namely, “capability foundation—application expansion—industrial deepening”. It further examines the development trends, major challenges, and system-level layout directions in this field. Overall, artificial intelligence-enabled materials innovation is currently in a transitional stage from proof of concept to systematic application. Front-end links such as materials property prediction, candidate screening, inverse design, and literature-based knowledge extraction have developed rapidly, whereas complex materials synthesis, experimental validation, service performance prediction, pilot-scale scale-up, and industrial process embedding remain major bottlenecks. In the future, it is necessary to promote coordinated development across the three implementation levels under the guidance of critical materials tasks, strengthen the innovation chain through synthesis validation, service performance prediction, and pilot-scale scale-up, and support large-scale expansion through platform-based and standardized systems.