Understanding element solution energies in nickelbase alloys using machine learning

Abstract

The design of nickelbase superalloys requires to tune the content of different phases with a composition of Ni _3 A, either to strengthen the alloy ( $\gamma ^{\prime} $ -phase, Ni _3 Al, γ ″-phase, Ni _3 Nb) or to influence its grain size and avoid embrittlement ( δ -phase, Ni _3 Nb, η -phase, Ni _3 Ti). Here, we use a machine-learning-inspired approach to understand the influence of elemental properties on the energy of an alloying element in the respective phases. It is shown that the energy in γ ″, δ , and η can be fitted well using the Bader charge and the volume of the element in a nickel matrix. In the case of $\gamma ^{\prime} $ , the small lattice mismatch requires a fit not involving the volume, but the bond order instead. We also show that the frequently used M _d -parameter can be predicted from the properties of an element in a pure nickel matrix. Finally, the physical basis of the results is discussed in detail.

Keywords

• nickel alloys
• nickelbase superalloys
• phase stability
• density functional theory
• machine learning