Brittleness is a critical issue hindering the potential application of the X2YZ-type full Heusler alloys in several fields of state-of-the-art technologies.To realize optimization of brittleness or design a ductile Heuser alloy,it is greatly urgent to identify the key materials factors deciding brittleness and establish an empirical rule to effectively evaluate ductility.For this purpose,by using a machine learning and human analysis cooperation approach,the brittleness of the X2YZ-type Heusler alloys was systematically studied.Results showed that the ductility is majorly decided by 6 key materials factors in the studied alloys.Using these 6 factors,a machine learning model to predict the Pugh's ratio k was constructed.Further analyses showed that the crystal structure of the X component could be the most critical factor deciding the ductility.The X component has the face-centered cubic(FCC)structure for most of the alloys with superior ductility.To effectively estimate ductility and guide materials design,an empirical formula of k=mEWFm+nGm+ko was established based on the known information of electron work function(EWF)and shear modulus(G)of the X,Y,and Z elements where the subscript m represents the weight-average value.The coefficients of m(negative)and n(positive)were confirmed to have opposite signs,which can be explained based on the relations between the ductility and the deformation/fracture resistance.This work is expected to deepen the understanding in ductility and promote the design of advanced ductile Heusler alloys.

Key Laboratory for Anisotropy and Texture of Materials(Ministry of Education),School of Material Science and Engineering,Northeastern University,Shenyang 110819,China;State Key Lab of Rolling and Automation,Northeastern University,Shenyang 110819,China;Beijing Advanced Innovation Center for Materials Genome Engineering,State Key Laboratory for Advanced Metals and Materials,University of Science and Technology Beijing,Beijing 100083,China;Laboratoire d'étude des Microstructures et de Mécanique des Matériaux(LEM3),CNRS UMR 7239,Université de Lorraine,Metz 57045,France

[1]Hao-Xuan Liu;Hai-Le Yan;Nan Jia;Shuai Tang;Daoyong Cong;Bo Yang;Zongbin Li;Yudong Zhang;Claude Esling;Xiang Zhao;Liang Zuo-.Machine-learning-assisted discovery of empirical rule for inherent brittleness of full Heusler alloys)[J].材料科学技术（英文版）,2022(36):1-13

Brittleness,X2YZ,Heuser,mEWFm+nGm+ko,subscript

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