Effects of Aluminum and Molybdenum on the Phase Stability of Iron-Chromium Alloys: A First-Principles Study

The interaction between solute atoms is critical to the thermodynamic behavior of Fe-Cr alloys, but the effects of non-dilute Al and Mo on the Fe-Cr phase stability and vacancy formation energy are not clearly understood. In this study, density functional theory, cluster expansion, and Monte Carlo simulation are used to predict the effects of ternary solute elements on the thermodynamic properties in multicomponent Fe-Cr alloys. The machine learning regression approach is applied to train and construct energy models that accurately describe the complex chemical interactions. The computational outcomes include the prediction of the partial ternary phase diagram, mixing enthalpy, and vacancy formation energy for different compositions. The phase boundary calculation predicts a pronounced change of Cr solubility in bcc Fe by the addition of Al and the rejection of Al atoms from α′ precipitates. The mixing enthalpy calculation shows that Mo may also reduce the Cr solubility in bcc Fe. Additionally, the simulations show that non-dilute Cr decreases the vacancy formation energy in bcc Fe, while adding Al results in a less significant effect. The results demonstrate important applications of using machine learning energy models to study model or commercial alloys with multicomponent solute species and point defects.