Machine Learning‐Guided Design of Cu–Ni–Sn Alloys With Tailored Ni 3 Sn/Ni 3 Sn 2 Precipitates

Abstract

To overcome the strength‐conductivity trade‐off in Cu–Ni–Sn alloys, this study integrates machine learning, thermodynamic calculations, and experiments to design alloys from a phase‐selectivity perspective. SHAP analysis reveals that the coexistence of Ni 3 Sn and Ni 3 Sn 2 precipitates yields synergistic strengthening superior to single‐phase precipitation. Feature screening identifies the variance of covalent radius and mean Allred–Rochow electronegativity as key physical parameters coupling mechanical and electrical properties. By combining a support vector regression model with thermodynamic phase‐equilibrium calculations, an optimal 350°C aging design window, corresponding to a Sn/(Ni + Sn) mass ratio of approximately 40%–56%, was identified for dual‐phase coexistence. Experimental validation of the predicted Cu–9Ni–9Sn alloy demonstrates a favorable property balance, achieving a tensile strength of 1351 MPa, hardness of 415 HV, and electrical conductivity of 12.43% IACS. Microstructural analysis confirms that the excellent performance originates from the multi‐scale dislocation obstruction by γ‐DO 22 /γ‐L1 2 ordered phases and Ni 3 Sn 2 precipitates, coupled with reduced electron scattering due to solute depletion. This closed‐loop paradigm offers a transferable route for multiphase regulation in precipitation‐strengthened alloys.