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.

主相中第二种元素 (α(Mg)) 的固溶行为在高性能合金的设计中很重要。在这项工作中，收集了三组特征：a）通过第一性原理计算获得的溶质和 Mg 的相互作用特征，b）纯元素的固有物理性质和 c）结构特征。根据最大固溶度值，将元素在α(Mg)中的溶解行为分为混溶、可溶、微溶和微溶四种类型。执行机器学习方法，包括随机森林和决策树算法方法，并发现四个特征，例如形成能、电负性、非键原子半径和功函数，可以共同确定元素在 α(Mg) 中的溶解行为的分类。分析了所选特征之间的数学相关性以及物理关系。如果需要，此模型还可以在对定义的功能进行细微修改后应用于其他系统。