English

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

Materials Science 2025-12-18 v1

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 1150\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 Ni17Co23Cr30Al30\mathrm{Ni_{17}Co_{23}Cr_{30}Al_{30}} as a substitute for conventional mutlilayer thermal barrier coatings.

Keywords

Cite

@article{arxiv.2512.15517,
  title  = {Machine-learned accelerated discovery of oxidation-resistant NiCoCrAl high-entropy alloys},
  author = {Dennis Boakye and Chuang Deng},
  journal= {arXiv preprint arXiv:2512.15517},
  year   = {2025}
}

Comments

Preprint submitted to Computational Materials Science