Deciphering the strength-ductility trade-off in (CuNiMn)-X alloys via interpretable machine learning

Existing machine learning-assisted alloy design studies often treat models as “black boxes,” lacking interpretability and thus failing to translate predictions into physically meaningful design guidelines. Herein, we propose an integrated strategy combining feature engineering, autonomous model optimization, and interpretability analysis for the efficient design of (CuNiMn)-X alloys, where X denotes Al, Ti, Cr, and Fe alloying elements, with strength-ductility synergy. Through feature cleaning and grid-based hyperparameter optimization, prediction accuracies of 92.6%, 89.2%, and 88.1% are achieved for the microstructure, compressive strength, and fracture strain models, respectively. Experimental validation of the optimized alloy shows errors of -5.0% for compressive strength and -3.2% for fracture strain. SHAP analysis reveals the underlying physical mechanisms: valence electron concentration (M-E10) governs phase selection; mean atomic radius (M-A4) and its variance dominate compressive strength via lattice distortion; variances of fusion enthalpy, covalent radius, and shear modulus collectively control compressive strain through triple homogeneity in thermodynamics, structure, and elasticity. This analysis unveils a mirror-image symmetry between high strength and high ductility in feature space, elucidating the strength-ductility trade-off. Guided by predictions, the (CuNiMn)-Al16Cr16Fe16 alloy is experimentally validated, exhibiting a compressive strength of 2542 MPa and fracture strain of 15.8%. The microstructure consists of Cr-rich BCC dendrite arms and a Cu-Ni-Fe-Al-rich FCC interdendritic network, forming a hard-and-tough dual-phase architecture. This closed-loop design strategy provides a new route for the efficient discovery of CuNiMn-based multi-principal element copper alloys and offers guidance for the broader design of complex multi-principal element alloys.