Resolving the strength-ductility trade-off in Al-Mg-Si alloys via physics-informed machine learning

Composition-based machine learning for Al-Mg-Si alloys lacks generalization. A physics-informed strategy incorporating physicochemical descriptors was proposed, improving the coefficient of determination (R2) by 30% over composition-only models. Shapley additive explanations (SHAP) identified electronegativity difference and average shear modulus mismatch as dominant descriptors, providing a mechanistic basis for optimizing the concentrations of Mg-Si-Cu for precipitates and Mn-Cr-Zr for dispersoid formation. Guided by non-dominated sorting genetic algorithm (NSGA-II), a novel alloy achieved an ultimate tensile strength of 442.2 MPa with 14.6% elongation. Microstructural analysis revealed that a uniform grain structure and high-density β″, Q′, and L co-precipitates resolved the strength-ductility trade-off.