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

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 (Ti-3(Al(1-x)A(x))C-2). The structure-property correlation between the A-site substitution and mechanical properties of Ti-3(Al(1-x)A(x))C-2 is established. The results show that the thermodynamic stability of Ti-3(Al(1-x)A(x))C-2 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.

Automated summary

In this study, a novel strategy based on the crystal graph convolution neural network (CGCNN) model has been successfully employed to tune the mechanical properties of Ti3AlC2-based MAX phases. The structure-property correlation between A-site substitution and mechanical properties of Ti-3(Al(1-x)A(x))C-2 is established. The results show that the thermodynamic stability, stiffness, thermal shock resistance, and plasticity of Ti3AlC2 can be improved by the substitution of different elements, providing universal guidance for the synthesis of high-performance MAX phases.