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 (Ti₃(Al_1-xA_x)C₂). The structure-property correlation between the A-site substitution and mechanical properties of Ti₃(Al_1-xA_x)C₂ is established. The results show that the thermodynamic stability of Ti₃(Al_1-xA_x)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.