Advancing refractory high entropy alloy development with AI-predictive models for high temperature oxidation resistance
(2025) Scripta Materialia — Vol. 255, p. 116394 (2025)
Files
gorsse2024.pdf
Open Access - Adobe PDF
- 6.39 MB
Details
- Authors
Gorsse, Stéphane 
Murakami, Hideyuki Yeh, An-Chou
- Abstract
- Refractory high-entropy alloys (RHEAs) and complex concentrated alloys (RCCAs) are vital for high-temperature applications beyond the capabilities of Ni-based superalloys. Traditional methods for predicting oxidation esistance in these alloys are often inaccurate and resource-intensive. This study introduces a novel approach using Gradient Boosted Decision Trees (GBDT), an artificial intelligence technique, to predict specific mass gain due to oxidation. Utilizing a dataset synthesized from extensive literature and characterized by diverse alloy compositions and oxidation conditions, the model was trained using Iterated Nested k-fold Cross Validation with Shuffling (INKCVS). Our findings demonstrate that the GBDT model achieves a good balance between accuracy and generalization capacity in predicting oxidation resistance, as validated experimentally with selected alloys. This approach not only enhances prediction accuracy but also significantly reduces the need for extensive experimental testing, facilitating rapid development of new high-performance materials.
- Affiliations
Citations
Gorsse, S., Lin, W.-C., Murakami, H., Rignanese, G.-M., & Yeh, A.-C. (2025). Advancing refractory high entropy alloy development with AI-predictive models for high temperature oxidation resistance. Scripta Materialia, 255, 116394. https://doi.org/10.1016/j.scriptamat.2024.116394 (Original work published 2025)