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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 Ti3AlC2-based MAX phases via the A-site substitution (Ti3(Al1-xAx)C2). The structure-property correlation between the A-site substitution and mechanical properties of Ti3(Al1-xAx)C2 is established. The results show that the thermodynamic stability of Ti3(Al1-xAx)C2 is enhanced with substitutions A = Ga, Si, Sn, Ge, Te, As, or Sb. The stiffness of Ti3AlC2 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 Ti3AlC2 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.


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Maximizing the mechanical performance of Ti3AlC2-based MAX phases with aid of machine learning

Show Author's information Xingjun DUANaZhi FANGaTao YANGaChunyu GUOaZhongkang HANbDebalaya SARKERcXinmei HOUa( )Enhui WANGa( )
Beijing Advanced Innovation Center for Materials Genome Engineering, Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, Berlin 14195, Germany
UGC-DAE Consortium for Scientific Research, University Campus, Khandwa Road, Indore 452001, India

† Xingjun Duan and Zhi Fang contributed equally to this work.

Abstract

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 Ti3AlC2-based MAX phases via the A-site substitution (Ti3(Al1-xAx)C2). The structure-property correlation between the A-site substitution and mechanical properties of Ti3(Al1-xAx)C2 is established. The results show that the thermodynamic stability of Ti3(Al1-xAx)C2 is enhanced with substitutions A = Ga, Si, Sn, Ge, Te, As, or Sb. The stiffness of Ti3AlC2 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 Ti3AlC2 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.

Keywords:

Ti3(Al1-xAx)C2, crystal graph convolution neural network (CGCNN) model, stability, mechanical properties
Received: 05 March 2022 Revised: 01 May 2022 Accepted: 13 May 2022 Published: 29 June 2022 Issue date: August 2022
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Publication history

Received: 05 March 2022
Revised: 01 May 2022
Accepted: 13 May 2022
Published: 29 June 2022
Issue date: August 2022

Copyright

© The Author(s) 2022.

Acknowledgements

This work was supported by the National Science Fund for Distinguished Young Scholars (No. 52025041), the National Natural Science Foundation of China (Nos. 51904021, 51974021, and 52174294), and the National Key R&D Program of China (No. 2021YFB3700400).

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