2026 Bu Machine Learning Al Li Alloy Design

Al-Li alloys, while offering low density and high specific strength, exhibit a pronounced trade-off between mechanical strength and solidification cracking resistance, significantly limiting their engineering applications. Traditional trial-and-error approaches prolong development cycles and incur high labor and material costs. Herein, we adopt a data augmentation-assisted machine learning (ML) strategy to accelerate the discovery of high-strength Al-Li alloy with enhanced cracking resistance. Given the scarcity of available data, various ML algorithms were compared, and linear regression (LR) was selected for its high predictive accuracy and robustness against overfitting. Based on this model, data augmentation was performed using SMOGN and CV AE methods. SHAP analysis identified that Cu and Sc are the most influential elements for strength, while cracking resistance is predominantly governed by Sc and Li. Under the constraint of low cracking volume, optimal compositions were predicted using LR, SMOGN and CV AE, followed by experimental validation. The alloy predicted by SMOGN achieves a superior yield strength of 412 MPa, exceeding that of current high crack-resistant Al-Li alloys. In contrast, the alloy designed by LR exhibits inferior mechanical performance.