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arXiv · 2512.15517

Machine-learned accelerated discovery of oxidation-resistant NiCoCrAl high-entropy alloys

Dennis Boakye·Chuang Deng
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Abstract

The development of oxidation-resistant high-entropy alloy (HEA) bond coats is restricted by the limited understanding of how multi-principal element interactions govern scale formation across temperatures. This study uncovers new oxidation trends in NiCoCrAl HEAs using a data-driven analysis of high-fidelity experimental oxidation data. The results reveal a clear temperature-dependent transition between alumina- and chromia-dominated protection, identifying the compositional regimes where alloys rich in Al dominate at $\ge1150$ {\deg}C, mixed Al-Cr chemistries are optimal at intermediate temperatures, and, unexpectedly, Cr-rich low-Al alloys perform best at 850 {\deg}C-challenging the assumption that high Al is universally required. The effects of Hf and Y are shown to be strongly composition-dependent with Hf producing the largest global reduction in oxidation rate, while Y becomes effective primarily in NiCo-lean alloys. Y-Hf co-doping offers consistent improvement but exhibits site-saturation behavior. These insights identify new high-performing HEA bond-coat families, including $\mathrm{Ni_{17}Co_{23}Cr_{30}Al_{30}}$ as a substitute for conventional mutlilayer thermal barrier coatings.

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Dennis Boakye, Chuang Deng. 2025-12-17. Machine-learned accelerated discovery of oxidation-resistant NiCoCrAl high-entropy alloys. https://arxiv.org/abs/2512.15517

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