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Meeting

2027 TMS Annual Meeting & Exhibition

Symposium

Computational Discovery and Design of Materials

Presentation Title

Machine Learning-Assisted Design of High-Performance HOR Catalysts for Proton-Conducting Fuel Cells

Author(s)

Sung Gu Kang

On-Site Speaker (Planned)

Sung Gu Kang

Abstract Scope

Developing active anode catalysts is significant for advancing proton-conducting fuel cells. In this study, we employ a machine-learning interatomic potential (MLIP) to efficiently identify multicomponent alloy catalysts for the hydrogen oxidation reaction (HOR). Promising candidates are examined using density functional theory (DFT), confirming hydrogen adsorption free energies close to the optimal range. To further improve catalytic performance, isolated noble-metal atoms are introduced onto the alloy surface, leading to more favorable hydrogen adsorption and enhanced HOR activity. This combined MLIP–DFT framework helps rapid screening and optimization of alloy catalysts and offers an efficient approach for designing high-performance HOR catalysts for proton-conducting fuel cells.

Proceedings Inclusion?

Planned:

Keywords

High-Entropy Alloys, Energy Conversion and Storage, Computational Materials Science & Engineering

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