Machine learning-driven inverse design and experimental validation of gradient diamond/Al composites for thermal stress mitigation

High-power electronic packaging demands thermal management materials combining high thermal conductivity with low thermal stress. Diamond (Dia)/Al composites are promising, but interfacial thermal expansion mismatch induces stress concentrations and thermal cycling damage that conventional empirical gradient design cannot resolve through trial-and-error. A closed-loop framework integrating mechanism anchoring, machine learning-driven optimization, and fabricability validation is proposed. Gradient configurations were fabricated via spark plasma sintering (SPS) and combined with three-dimensional finite element simulation to establish an empirical baseline. A multilayer perceptron (MLP) surrogate coupled with the non-dominated sorting genetic algorithm II(NSGA-II) then performed inverse optimization across the full 10–90 vol% design space to minimize thermal stress, followed by independent high-fidelity validation. An optimized formulation was selected under fabricability constraints, fabricated under the same SPS conditions, and validated through post-thermal-cycling SEM and EDS comparison. The surrogate achieves a mean absolute error of 2.73 MPa for maximum thermal stress prediction, with an independent validation R 2 of 0.933 over 80 Pareto solutions. The optimized formulation reduces maximum thermal stress by approximately 77% (from 136 to 31.2 MPa) and increases post-thermal-cycling relative density by 2.97 (from 83.47% to 86.44%). Post-thermal-cycling SEM and EDS confirm crack-free interfaces in the optimized formulation, in contrast to the interfacial gaps and microcracks observed in the baseline. This closed-loop methodology provides a reproducible pathway from machine learning-accelerated inverse design to experimental validation, supporting the transition of Dia/Al packaging materials from empirical trial-and-error to quantitative design.