The world’s first big data and intelligent design platform for magnesium materials, “MagNova”, jointly developed by Mingyue Lake Laboratory, Chongqing University, and the National Engineering Research Center for Magnesium Alloys, was officially launched.

The performance of Mg alloys is significantly influenced by the concentrations and solid solution behavior of the alloying elements. In this work, the solid solution behavior of 20 alloying elements in 190 ternary Mg alloy systems at 500 ℃ are systematically investigated. The solid solution behavior of a set of two different alloying elements in Mg alloy systems are suggested to be classified into three categories: inclusivity, exclusivity and proportionality. Inclusivity classification indicates that the two alloying elements are inclusive in α-Mg, increasing the joint solubility of both elements. Exclusivity classification suggests that the two alloying elements have a low joint solid solubility in α-Mg, since they prefer to form stable second phases. For the proportionality classification, the solubility curve of the ternary Mg alloy systems is a straight line connecting the solubility points of the two sub-binary systems. The proposed classification theory was validated by key experiments and the calculation of formation energies. The interaction effects between alloying elements and the preference of formation of second phases are the main factors determining the solid solution behavior classifications. Based on the observed solid solution features of multi-component Mg alloys, principles for alloy design of different types of high-performance Mg alloys were proposed in this work.

The solution behavior of a second element in the primary phase (α(Mg)) is important in the design of high-performance alloys. In this work, three sets of features have been collected: a) interaction features of solutes and Mg obtained from first-principles calculation, b) intrinsic physical properties of the pure elements and c) structural features. Based on the maximum solid solubility values, the solution behavior of elements in α(Mg) are classified into four types, e.g., miscible, soluble, sparingly-soluble and slightly-soluble. The machine learning approach, including random forest and decision tree algorithm methods, is performed and it has been found that four features, e.g., formation energy, electronegativity, non-bonded atomic radius, and work function, can together determine the classification of the solution behavior of an element in α(Mg). The mathematical correlations, as well as the physical relationships among the selected features have been analyzed. This model can also be applied to other systems following minor modifications of the defined features, if required.