Stacking fault energies on {112} planes of an AlNbTaTiV BCC high-entropy alloy from first-principles calculations, analyzed with inferential statistics

Abstract

BCC refractory high-entropy alloys (HEAs) are of interest to the materials science community due to their promising high temperature properties. Stacking fault energy (SFE) is investigated in the present work due to its use in modeling mechanical and deformation behavior, such as nano-twinning. The possible stacking faults of the BCC {112} plane are reviewed and classified with a new naming convention of TSn, twinning sense, and NTSn, non-twinning sense. Five stacking faults are identified and investigated in the present work. Density functional theory and special quasirandom structures are used to calculate the SFE of a candidate single-phase BCC AlNbTaTiV HEA. The SFE for each fault are calculated for several pure elements and compared to computational and experimental literature when available. Inferential statistics allows the properties of the total population to be predicted from a sample set of calculations. The results indicate that a sample of only 20 values are required for precise calculations. Inferential statistics are then used to place predictive error bars on the HEA SFE calculations and sampling procedure. All sample SFE values were well within the predictive error bars when compared to the SFE calculated from the total population. Finally, inferential statistics parameters are used to predict the properties of a global population based on a sample set of calculations, demonstrating the usefulness of the technique developed here for application to materials property database generation.