Accelerated optimal design of high-performance Mg alloys through integration of Bayesian optimization with active machine learning

This research investigates the accelerated design of high-performance magnesium (Mg) alloys by integrating Bayesian optimization with active machine learning. The dataset encompasses various alloy compositions and processing parameters, including Zn, Y, Y, Zr, Nd, and Gd concentrations, along with solution treatment, homogenization, extrusion, and aging conditions. Mechanical properties such as Ultimate Tensile Strength (UTS), Yield Strength (YS), and Elongation (EL) are also recorded. Key findings indicate that increased Zn content generally enhances UTS and YS, though at the expense of elongation. Bayesian optimization and machine learning techniques applied to this data enable efficient identification of optimal alloy compositions and processing parameters, significantly accelerating the development of high-performance Mg alloys.