Machine-learning-guided tungsten single atoms promote oxyhydroxides for noble-metal-free water electrolysis
Year: 2026
Type: article
Abstract: Lowering the overpotential of oxygen evolution reaction with electrocatalysts is essential for efficient renewable-electricity-driven electrolysis. Active noble-metal catalysts suffer from leaching and scarcity, while non-noble alternatives face limited intrinsic activity. Here we combine computational guidance with experimental validation to identify atomically dispersed tungsten within NiFe oxyhydroxide, namely W1-NiFeOOH, as a promising noble-metal-free oxygen evolution reaction catalyst. An equivariant transformer-based machine-learning interatomic potential accelerates out-of-domain adsorption energy predictions and nominates W1-NiFeOOH from 3,976 single-atom-incorporated metal oxyhydroxide configurations. Cyclic-electrodeposited W1-NiFeOOH achieves a high current density of 13.1 A cm-2 at 2.0 V and remains stable for 500 hours in alkaline exchange-membrane water electrolysis with commercial membranes. In situ spectroscopy and density functional theory calculations suggest that subsurface W promoter induces synergistic electron redistribution at neighboring Ni-O-Fe edge active sites, thereby lowering the proton-coupled electron-transfer barrier for the deprotonation step and facilitating transformation into the active γ-phase. This integrated computational-experimental workflow provides a blueprint for cost-effective catalyst design for sustainable energy systems. Sustainable hydrogen production requires efficient oxygen evolution catalysts. Here, the authors identify non-noble single-atom catalysts via machine learning-assisted computational screening and reveal their active-site mechanisms for high-performance anion exchange membrane water electrolysis.
Source: Nature Communications
Institutions: Seoul National University, Kunsan National University, Pohang University of Science and Technology, Pohang Accelerator Laboratory, Korea Research Institute of Standards and Science, California Institute of Technology, Korea University, Korea University of Science and Technology, Incheon National University, Advanced Institute of Convergence Technology
Language: English
Field: Energy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: National Research Foundation, Seoul National University, Korea Research Institute of Standards and Science, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute of Engineering Research, Seoul National University, National Supercomputing Center, Korea Institute of Science and Technology Information
OpenAlex ID: W7126099952