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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 resistance 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.

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• In Copyright
  

  This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11085-024-10313-3.

Keyword: Oxidation properties, Additive manufacturing, High entropy alloys

Date published: 2024-09-23

Publisher: Springer Science and Business Media LLC

Journal:

• High Temperature Corrosion of Materials (ISSN: 27318397) vol. 101 issue. 6 p. 1369-1379

Funding:

• Ministry of Education (MOE) in Taiwan.
• National Science and Technology Council (NSTC) in Taiwan 112-2927-I-007-504

Manuscript type: Author's version (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5247

First published URL: https://doi.org/10.1007/s11085-024-10313-3

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Updated at: 2025-09-23 08:30:15 +0900

Published on MDR: 2025-09-23 08:20:54 +0900