Artificial intelligence‐enabled synergistic design of strength and stress corrosion cracking resistance in light alloys

Abstract High‐performance light alloys, including aluminum, titanium, magnesium alloys, etc., are utilized in aerospace, aviation, transportation and medical applications. A key challenge for these alloys is achieving both improved strength and stress corrosion cracking (SCC) resistance by optimizing the relationships between composition, processing, microstructure, and macroscopic properties. Artificial intelligence (AI)‐driven multi‐modal machine learning offers opportunities for materials design and prediction. Proposed strategies include applying machine learning‐based approaches for concurrent improvement of alloy strength and SCC resistance, conducting in situ high‐throughput experiments to investigate SCC microcrack initiation mechanisms under combined mechanical, microstructural, and corrosion conditions to support database development and developing correlative AI models for alloy microstructure evolution and macroscopic SCC failure behavior in service environments.