About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Environmental Degradation of Multiple Principal Component Materials
|
| Presentation Title |
Machine-Learned Accelerated Discovery of
Oxidation-Resistant NiCoCrAl High-Entropy Alloys |
| Author(s) |
Dennis Boakye, Chuang Deng |
| On-Site Speaker (Planned) |
Dennis Boakye |
| Abstract Scope |
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 un-covers new oxidation trends in NiCoCrAl HEAs using a data-driven anal-
ysis 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 °C, mixed Al–Cr chemistries are optimal at intermediate temperatures, and, unexpectedly, Cr-rich low-Al alloys perform best at 850 °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 as a substitute for conventional mutlilayer thermal barrier coatings. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, High-Entropy Alloys, Machine Learning |