Highly effective design of high GFA alloys with different metalbased and various components by machine learning  

作　　者：TANG YiChuan HE YiFan FAN ZhuoQun WANG ZhongQi TANG ChengYing 

机构地区：[1]Guangxi Key Laboratory for Informational Materials,School of Materials Science and Engineering,Guilin University of Electronic Technology,Guilin,541004,China

出　　处：《Science China(Technological Sciences)》2024年第5期1431-1442,共12页中国科学（技术科学英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.51761005);the Sino-German Science Centre(Grant No.GZ1002);Guangxi Natural Science Foundation(Grant No.2019GXNSFAA245004);the Talents Project of Guilin University of Electronic Technology。

摘　　要：The glass-forming ability(GFA)is of great significance for the development of novel functional metal-based metallic glasses.In this study,seven popular machine learning(ML)algorithms were employed to design novel M-based(M=Fe,Co,Ni,Ti,Zr,and rare earth metal(RE))and X-component(X=2,3,4,5,6,and>6)alloys with excellent GFA.A GFA containing 6957 data points with structural analysis was established.Feature engineering was used to analyze the importance and correlation of features.ML algorithms were utilized for GFA prediction,revealing that Xtreme Gradient Boosting Trees exhibited the strongest predictive capability,achieving a high accuracy of 94.0%,a true positive rate of 97.6%,and a root mean squared error of 0.3705 across the entire dataset.Subsequently,the GFA of ternary to hexahydroxy alloys based on Fe,Co,Ni,Zr,Ti,and Y was predicted using all possible compositions generated through Python.Finally,a series of alloys with good GFA was successfully designed and prepared.The present work suggests that the proposed ML method can be utilized to design novel multiple-M-based amorphous alloys with high GFA.

关 键 词：machine learning amorphous alloy feature engineering materials design glass forming ability 

分 类 号：TG139.8[一般工业技术—材料科学与工程] TP181[金属学及工艺—合金]