Using high-throughput first-principles calculations, we systematically studied the synergistic effect of alloying two elements （AI and 28 kinds of 3d, 4d, and 5d transition metals） on the elastic constants and elast...Using high-throughput first-principles calculations, we systematically studied the synergistic effect of alloying two elements （AI and 28 kinds of 3d, 4d, and 5d transition metals） on the elastic constants and elastic moduli of γ-Ni. We used machine learning to theoretically predict the relationship between alloying concentration and mechanical properties, giving the binding energy between the two elements. We found that the ternary alloying elements strengthened the 7 phase in the order of Re 〉 Ir 〉 W 〉 Ru 〉 Cr 〉 Mo 〉 Pt 〉 Ta 〉 Co. There is a quadratic parabolic relationship between the number of d shell electrons in the alloying element and the bulk modulus, and the maximum bulk modulus appears when the d shell is half full. We found a linear relationship between bulk modulus and alloying concentration over a certain alloying range. Using linear regression, we found the linear fit concentration coefficient of 29 elements. Using machine learning to theoretically predict the bulk modulus and lattice constants of Ni32XY, we predicted values close to the calculated results, with a regression parameter of R2 = 0.99626. Compared with pure Ni, the alloyed Ni has higher bulk modulus B, G, E, Cll, and C44, but equal Cl2. The alloying strengthening in some of these systems is closely tied to the binding of elements, indicating that the binding energy of the alloy is a way to assess its elastic properties.展开更多
基金Project support by the National Key R&D Program of China(Grant Nos.2017YFB0701501,2017YFB0701502,and 2017YFB0701503)
文摘Using high-throughput first-principles calculations, we systematically studied the synergistic effect of alloying two elements （AI and 28 kinds of 3d, 4d, and 5d transition metals） on the elastic constants and elastic moduli of γ-Ni. We used machine learning to theoretically predict the relationship between alloying concentration and mechanical properties, giving the binding energy between the two elements. We found that the ternary alloying elements strengthened the 7 phase in the order of Re 〉 Ir 〉 W 〉 Ru 〉 Cr 〉 Mo 〉 Pt 〉 Ta 〉 Co. There is a quadratic parabolic relationship between the number of d shell electrons in the alloying element and the bulk modulus, and the maximum bulk modulus appears when the d shell is half full. We found a linear relationship between bulk modulus and alloying concentration over a certain alloying range. Using linear regression, we found the linear fit concentration coefficient of 29 elements. Using machine learning to theoretically predict the bulk modulus and lattice constants of Ni32XY, we predicted values close to the calculated results, with a regression parameter of R2 = 0.99626. Compared with pure Ni, the alloyed Ni has higher bulk modulus B, G, E, Cll, and C44, but equal Cl2. The alloying strengthening in some of these systems is closely tied to the binding of elements, indicating that the binding energy of the alloy is a way to assess its elastic properties.
