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...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 inα(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 inα(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.展开更多
基金the financial support from the National Natural Science Foundation of China(51971044 and U1910213)Natural Science Foundation of Chongqing(cstc2019yszx-jcyj X0004)Fundamental Research Funds for the Central Universities(2020CDJDPT001)。
文摘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 inα(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 inα(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.
