Machine learning accelerated discovery of corrosion-resistant high- entropy alloys

C Zeng and A Neils and J Lesko and N Post, COMPUTATIONAL MATERIALS SCIENCE, 237, 112925 (2024).

DOI: 10.1016/j.commatsci.2024.112925

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 the compactness of oxide films formed on an alloy surface evaluated by 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 from a wide range of compositions. 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 provides a computationally efficient approach to navigate high -dimensional composition space of high -entropy alloys. It is general in its application and applicable to other complex materials, enabling high -throughput screening of material candidates and potentially accelerating the iteration of integrated computational materials engineering.

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