Abstract
Magnesium (Mg) has a strong application potential as a lightweight metal. Yet, its absolute strength still needs improvement. In this work, we demonstrate that machine learning can be utilized to guide the development of high-strength Mg cast alloys. In the design framework, the composition and heat treatment condition are iteratively optimized by a surrogate model that is also evolving. After two iterations, a new alloy with the composition of Mg-10.0Al-2.0Sn-2.0Zn-0.1Ca-0.1Mn (at. pct) was identified. After aging at 200 °C for 96 hours, this alloy shows a Vickers hardness value of 110.5 Hv, which surpasses the highest value (102.5 Hv) in the initial dataset from literature. Finally, microstructure of the optimized alloy was characterized to understand the origin of its high hardness. This work demonstrates the potential of data-driven approaches for material development.
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