Designing high elastic modulus magnesium-based composite materials via machine learning approach

Magnesium alloys, renowned for their lightweight characteristics, are widely utilized in industries such as aerospace and automotive. However, the limited modulus of these alloys restrains their suitability for applications demanding robustness and high strength. The demand for magnesium materials with increased moduli has been steadily increasing as a result. Currently, alloying, and composite material techniques serve as the focal approaches for augmenting the elastic modulus of magnesium alloys. The modulus enhancement capacity of magnesium alloys is limited, failing to reach the desired levels, while composite materials demonstrate a greater ability to achieve higher moduli. The present scholarly discourse endeavors to furnish a comprehensive overview of methodologies employed for bolstering the modulus of magnesium-based composites, while also introducing the application of machine learning in the materials domain to explore its potential in improving the modulus of magnesium-based composites.