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Oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) are three critical reactions for energy-related applications, such as water electrolyzers and metal-air batteries. Graphene-supported single-atom catalysts (SACs) have been widely explored; however, either experiments or density functional theory (DFT) computations cannot screen catalysts at high speed. Herein, based on DFT computations of 104 graphene-supported SACs (M@C3, M@C4, M@pyridine-N4, and M@pyrrole-N4), we built up machine learning (ML) models to describe the underlying pattern of easily obtainable physical properties and limiting potentials (errors = 0.013/0.005/0.020 V for ORR/OER/HER, respectively), and employed these models to predict the catalysis performance of 260 other graphene-supported SACs containing metal-NxCy active sites (M@NxCy). We recomputed the top catalysts recommended by ML towards ORR/OER/HER by DFT, which confirmed the reliability of our ML model, and identified two OER catalysts (Ir@pyridine-N3C1 and Ir@pyridine-N2C2) outperforming noble metal oxides, RuO2 and IrO2. The ML models quantitatively unveiled the significance of various descriptors and fast narrowed down the potential list of graphene-supported single-atom catalysts. This approach can be easily used to screen and design other SACs, and significantly accelerate the catalyst design for many other important reactions.
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Toward Understanding the Stability of Single‐Atom Catalysts in Oxygen Evolution Reactions
Abstract Single‐atom catalysts (SACs) hold great promise for enhancing the efficiency of the oxygen evolution reaction (OER) by enabling precise control over atomic design and maximizing metal atom utilization. While recent reviews have primarily focused on structural regulation techniques to improve SAC stability, this review emphasizes the less explored but critical aspect of SAC stability. By examining stability and aggregation mechanisms, we complements design‐focused perspectives with insights into stability prediction and experimental interdependencies. We highlight advances in SACs where metal atoms are coordinated through N and O moieties, identifying support interactions, electron number, and the stability number (S‐number) as key stability descriptors. We also propose modified local geometries to mitigate aggregation and enhance stability. A comprehensive discussion of experimental validation, spectroscopy, molecular models, and computational studies provides a deeper understanding of the principles governing SAC stability. Future directions include exploring co‐catalyst metal‐support interactions, defects, and OH‐modified surfaces to further improve stability, paving the way for the next generation of OER electrocatalysts.
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- Award ID(s):
- 2503349
- PAR ID:
- 10596097
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Energy Materials
- Volume:
- 15
- Issue:
- 29
- ISSN:
- 1614-6832
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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