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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Machine learning and high-throughput computational guided development of high temperature oxidation-resisting Ni-Co-Cr-Al-Fe based high-entropy alloys
Author(s) Shanshan Hu, Xingru Tan, Michael Gao
On-Site Speaker (Planned) Shanshan Hu
Abstract Scope Hydrogen-fueled gas turbines are important for achieving carbon-neutral technology to meet ambitious energy and climate goals. But hydrogen combustion will significantly decrease the life span of current state-of-the-art MCrAlY coated turbines due to the higher flame temperature and the presence of water vapor. Herein, with the aid of machine learning and high throughput CALPHAD calculations, we successfully developed several NiCoFeCrAl based HEA coatings which achieved the same performance in hydrogen combustion environment as current systems in a natural gas environment. The HEA coating facilitates the formation of a protective scale of alpha-alumina to slow down the inward diffusion of oxidizing species and the outward diffusion of metal elements and possesses ultrahigh corrosion and spallation resistance to prolong the service lifetime of critical components in hydrogen turbine power system.
Proceedings Inclusion? Planned:
Keywords Machine Learning, High-Entropy Alloys, Computational Materials Science & Engineering

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