Data-driven design of novel lightweight refractory high-entropy alloyswith superb hardness and corrosion resistance  

作　　者：Tianchuang Gao Jianbao Gao Shenglan Yang Lijun Zhang 

机构地区：[1]State Key Laboratory of Powder Metallurgy,Central South University,Changsha,Hunan,China [2]State Key Laboratory of Materials Processing and Die&Mould Technology,Huazhong University of Science and Technology,Wuhan,Hubei,China [3]National Engineering Research Center for Magnesium Alloys,Chongqing University,Chongqing,China

出　　处：《npj Computational Materials》2024年第1期536-548,共13页计算材料学(英文)

基　　金：The financial support from the Natural Science Foundation of Hunan Province for Distinguished Young Scholars,China[GrantNo.2021JJ10062];the Science and Technology Program of Guangxi province,China[GrantNo.AB21220028];the Youth Fund of the National Natural Science Foundationof China[Grant No.52401047,Grant No.52401004];the China Postdoctoral Science Foundation,China[GrantNo.2023M741244];the Fundamental Research Funds for the Central Universities of Central South University,China[GrantNo.2023ZZTS0711]are acknowledged;The Project supported by State Key Laboratory of Powder Metallurgy,Central South University,Changsha,China is also acknowledged.

摘　　要：Lightweight refractory high-entropy alloys(LW-RHEAs)hold significant potential in the fields of aviation,aerospace,and nuclear energy due to their low density,high strength,high hardness,and corrosion resistance.However,the enormous composition space has severely hindered the development of novel LW-RHEAs with excellent comprehensive performance.In this paper,an machine learning(ML)-based alloy design strategy combined with a multi-objective optimization method was proposed and applied for a rational design of Al-Nb-Ti-V-Zr-Cr-Mo-Hf LW-RHEAs.The quantitative relation of“composition-structure-property”was first established by ML modeling.Then,feature analysis reveals that Cr content greater than 12 at.

关 键 词：corrosion alloys resistance 

分 类 号：TG1[金属学及工艺—金属学]