Computational strategies for high strength and thermal conductivity casting Mg/Al alloys design

High strength and high thermal conductivity Mg/Al alloys are pivotal for lightweight thermal management in aerospace and electric vehicle applications, yet their development is hindered by the intrinsic trade-off between solute strengthening and electron scattering. This work reviews the design approaches evolution from empirical trial-and-error to advanced Artificial Intelligence (AI). We first examine the physical mechanisms governing strength and thermal conductivity, identifying “matrix purification” and specific precipitation architectures as key microstructural design goals to minimize solute scattering. Then, the review critically evaluates traditional computational tools, including CALPHAD for phase equilibrium and Density Functional Theory for intrinsic transport predictions. Crucially, we demonstrate how integrating these physics-based inputs as features into Machine Learning models significantly enhances prediction accuracy for complex multicomponent systems. Subsequently, the integration of these approaches for the concurrent optimization of strength and thermal conductivity is discussed, highlighting the role of multi-objective optimization algorithms in mapping the Pareto frontier. Finally, the review discusses the future potential of emerging frontiers in Generative AI (GANs, VAEs) and inverse design, highlighting the critical role of expert knowledge–guided constraints and physics-informed priors in steering model training and generation. Such hybrid frameworks are envisioned to autonomously navigate high-dimensional compositional spaces while maintaining physical interpretability and thermodynamic consistency, thereby accelerating the discovery of next-generation multifunctional alloys.