  The design of corrosion-resistant high entropy alloys (CR-HEAs) is
challenging due to the alloys' virtually astrological composition space. To
facilitate this, efficient and reliable high-throughput exploratory approaches
are needed. Toward this end, the current work reports a first-principles-based
approach exploiting the correlations between work function, surface energy, and
corrosion resistance (i.e., work function and surface energy are, by
definitions, proportional and inversely proportional to an alloy's inherent
corrosion resistance, respectively). Two Bayesian CALPHAD models (or databases)
of work function and surface energy of FCC Co-Cr-Fe-Mn-Mo-Ni are assessed using
discrete surface energies and work functions derived by density-functional
theory (DFT) calculations. The models are then used to rank different
Co-Cr-Fe-Mn-Mo-Ni alloy compositions. It is observed that the ranked alloys
possess chemical traits similar to previously studied corrosion-resistance
alloys, suggesting that the proposed approach can be used to reliably screen
HEAs with potentially good inherent corrosion resistance.
