|About this Abstract
||MS&T22: Materials Science & Technology
||High Entropy Materials: Concentrated Solid Solutions, Intermetallics, Ceramics, Functional Materials and Beyond III
||Modeling of Oxidation Resistance in Ni-containing High Entropy Alloys: A Combined First-principles and Machine Learning Study
||Shun-Li Shang, Yi Wang, Zi-Kui Liu, Michael C. Gao
|On-Site Speaker (Planned)
Oxidation involves diffusion of oxygen and formation of oxides. These processes could lead to severe degradation of materials performance and hence appeal for understanding and predictive modeling of oxidation resistance through thermodynamics and kinetics. Taking Ni-containing high entropy alloys (HEAs) as an example, we examine the predicted diffusivity and the underlaying physics of atoms (especially oxygen) in nickel affected by alloying elements in terms of a combined transition state energetics based on first-principles calculations and machine learning analyses. In addition, the CALPHAD modeling approach is used to predict a compositional feasibility map to identify the formation of dense oxides (Al2O3 and Cr2O3) and other oxides. Knowledge of diffusion and feasibility map makes it possible to understand, predict, and optimize oxidation resistance as demonstrated in Ni-containing HEAs.