A First-Principles-Based Approach to The High-Throughput Screening of Corrosion-Resistant High Entropy Alloys

TL;DR

This paper introduces a first-principles-based high-throughput screening method for corrosion-resistant high entropy alloys, leveraging correlations between work function, surface energy, and corrosion resistance to efficiently identify promising alloy compositions.

Contribution

It develops a Bayesian CALPHAD modeling approach using DFT data to predict and rank corrosion resistance of FCC Co-Cr-Fe-Mn-Mo-Ni alloys, enabling reliable screening.

Findings

The models successfully rank alloys with traits similar to known corrosion-resistant alloys.

The approach demonstrates potential for efficient discovery of corrosion-resistant HEAs.

Predicted alloy rankings align with experimental corrosion resistance traits.

Abstract

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.