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Maximizing the mechanical performance of Ti_(3)AlC_(2)-based MAX phases with aid of machine learning 被引量:5
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作者 Xingjun DUAN Zhi FANG +5 位作者 Tao YANG Chunyu GUO Zhongkang HAN Debalaya SARKER Xinmei HOU Enhui WANG 《Journal of Advanced Ceramics》 SCIE EI CAS CSCD 2022年第8期1307-1318,共12页
Mechanical properties consisting of the bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,etc.,are key factors in determining the practical applications of MAX phases.These mechanical properties are mainly ... Mechanical properties consisting of the bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,etc.,are key factors in determining the practical applications of MAX phases.These mechanical properties are mainly dependent on the strength of M–X and M–A bonds.In this study,a novel strategy based on the crystal graph convolution neural network(CGCNN)model has been successfully employed to tune these mechanical properties of Ti_(3)AlC_(2)-based MAX phases via the A-site substitution(Ti_(3)(Al1-xAx)C_(2)).The structure–property correlation between the A-site substitution and mechanical properties of Ti_(3)(Al1-xAx)C_(2)is established.The results show that the thermodynamic stability of Ti_(3)(Al1-xAx)C_(2)is enhanced with substitutions A=Ga,Si,Sn,Ge,Te,As,or Sb.The stiffness of Ti_(3)AlC_(2)increases with the substitution concentration of Si or As increasing,and the higher thermal shock resistance is closely associated with the substitution of Sn or Te.In addition,the plasticity of Ti_(3)AlC_(2)can be greatly improved when As,Sn,or Ge is used as a substitution.The findings and understandings demonstrated herein can provide universal guidance for the individual synthesis of high-performance MAX phases for various applications. 展开更多
关键词 Ti_(3)(Al1−xAx)C_(2) crystal graph convolution neural network(CGCNN)model stability mechanical properties
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