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 |
Designing oxidation-resistant bond coats from high-entropy alloys (HEAs) has been a serious concern primarily due to the limited understanding of how multiprincipal element interactions govern scale formation at extreme temperatures. Moreover, the use of conventional trial-and-error approaches is inefficient due to the wide possibilities of element combinations, environmental conditions, and other processing variables. In this study, a machine learning framework is trained on high-fidelity experimental data to predict the parabolic oxidation constant (𝑘𝑝) as a measure of oxidation resistance. The model not only achieves state-of-the-art precision (𝑅2 = 0.91) but also reveals new oxidation trends in NiCoCrAl HEAs. The predictions reveal a clear, temperature-dependent transition in oxidation control, with Al-rich compositions exhibiting the lowest oxidation rates above 1100 ◦C. Compositions containing mixed Al-Cr ratios perform best at intermediate temperatures, and Cr-rich, low-Al alloys exhibit superior resistance at 850 ◦C. The incorporation of Hf and Y led to composition-dependent improvements, with Hf-added alloys exhibiting the lowest 𝑘𝑝. The effectiveness of Y was observed in NiCo-lean alloys, while Y-Hf co-doped alloys showed saturated improvements. These results provide quantitative
guidance for narrowing the compositional space of NiCoCrAl and identifying oxidation-resistant bond coats across service-relevant temperatures. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, High-Entropy Alloys, Machine Learning |