The intrinsic strength prediction by machine learning for refractory high entropy alloys

作     者：Yong-Gang Yan Kun Wang 

作者机构：Kazuo Inamori School of EngineeringAlfred UniversityAlfredNY14802USA 

出 版 物：《Tungsten》 (钨科技（英文）)

年 卷 期：2023年第5卷第4期

页      面：531-538页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Faculty Startup Fund in the New York State College of Ceramics at Alfred University 

主　　题：Refractory high entropy alloys Solid solution strengthening Machine learning Lattice distortion Heat of fusion 

摘      要：Herein,we trained machine learning(ML)model to quickly and accurately conduct the strength prediction of refractory high entropy alloys(RHEAs)*** Boosting(GB)regression model shows an outstanding performance against other ML *** addition,the heat of fusion and atomic size difference is shown to be paramount to the strength of the high entropy alloys(HEAs)*** addition,we discussed the contribution of each feature to the solid solution strengthening(SSS)of HE *** excellent predictive accuracy shows that the GB model can be efficient and reliable for the design of RHEAs with desired strength.