  Corrosion has a wide impact on society, causing catastrophic damage to
structurally engineered components. An emerging class of corrosion-resistant
materials are high-entropy alloys. However, high-entropy alloys live in
high-dimensional composition and configuration space, making materials designs
via experimental trial-and-error or brute-force ab initio calculations almost
impossible. Here we develop a physics-informed machine-learning framework to
identify corrosion-resistant high-entropy alloys. Three metrics are used to
evaluate the corrosion resistance, including single-phase formability, surface
energy and Pilling-Bedworth ratios. We used random forest models to predict the
single-phase formability, trained on an experimental dataset. Machine learning
inter-atomic potentials were employed to calculate surface energies and
Pilling-Bedworth ratios, which are trained on first-principles data fast
sampled using embedded atom models. A combination of random forest models and
high-fidelity machine learning potentials represents the first of its kind to
relate chemical compositions to corrosion resistance of high-entropy alloys,
paving the way for automatic design of materials with superior corrosion
protection. This framework was demonstrated on AlCrFeCoNi high-entropy alloys
and we identified composition regions with high corrosion resistance. Machine
learning predicted lattice constants and surface energies are consistent with
values by first-principles calculations. The predicted single-phase formability
and corrosion-resistant compositions of AlCrFeCoNi agree well with experiments.
This framework is general in its application and applicable to other materials,
enabling high-throughput screening of material candidates and potentially
reducing the turnaround time for integrated computational materials
engineering.
