Highly effective design of high GFA alloys with different metal-based and various components by machine learning

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外文摘要：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.

外文关键词：

machine learningamorphous alloyfeature engineeringmaterials designglass forming ability

作者：

TANG YiChuan、HE YiFan、FAN ZhuoQun、WANG ZhongQi、TANG ChengYing

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作者单位：

Guangxi Key Laboratory for Informational Materials,School of Materials Science and Engineering,Guilin University of Electronic Technology,Guilin 541004,China

基金：

National Natural Science Foundation of ChinaSino-German Science CentreGuangxi Natural Science FoundationTalents Project of Guilin University of Electronic Technology

项目编号：

51761005GZ10022019GXNSFAA245004

出版年：

2024

DOI：

10.1007/s11431-023-2490-4

中国科学:技术科学(英文版)

中国科学院

中国科学:技术科学(英文版)

CSTPCDEI

影响因子：1.056

ISSN：1674-7321

年,卷(期)：2024.67(5)