Data-driven discovery of copper alloys: resolving the strength-conductivity paradox

Abstract Achieving a simultaneous improvement in hardness (HV) and electrical conductivity (EC) remains a critical challenge in the development of copper alloys, where strengthening mechanisms often compromise conductivity. In the present work, we employed an integrated design strategy combining Bayesian optimization with an extreme gradient boosting predictive model to optimize both alloy composition and processing parameters in a Cu–Cr–Zr system. This approach identified a Cu–0.88Cr–0.14Zr (wt.%) alloy as the optimal composition, with solution treatment at 1213 K ( T ss ) and aging at 873 K ( T ag ) tuned to balance precipitation hardening and conductivity retention. CALPHAD based thermodynamic calculations using the Cu–Cr–Zr model parameters indicate that Cr-rich phases and Cu 5 Zr precipitates are stable under the selected conditions. These predictions were further validated through experimental synthesis and characterization. The alloy exhibited a HV of 247.4 HV and an EC of 76.8% IACS. Microstructural examination revealed a fine and homogeneous dispersion of nanoscale precipitates, which effectively contributed to the simultaneous enhancement of mechanical and electrical properties. This combined computational-experimental framework offers a promising pathway for designing copper alloys with potential applications in advanced electrical and electronic systems that require high strength and conductivity.