Collectivity effect in cluster catalysis under operational conditions

Jia-Lan Chen, Xue-Chun Jiang, Li Feng, Jinze Zhu, Jian-Wen Zhao, Jin-Xun Liu () and Wei-Xue Li ()
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Jia-Lan Chen: University of Science and Technology of China
Xue-Chun Jiang: University of Science and Technology of China
Li Feng: University of Science and Technology of China
Jinze Zhu: University of Science and Technology of China
Jian-Wen Zhao: University of Science and Technology of China
Jin-Xun Liu: University of Science and Technology of China
Wei-Xue Li: University of Science and Technology of China

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract Subnanometer metal clusters are promising catalysts but are limited by structural heterogeneity and dynamics under operational conditions. Herein, we employ artificial intelligence-enhanced multiscale modeling, integrated with statistical analysis, to exhaustively explore the catalytic sites of cluster catalysts under reaction conditions. We discover that numerous sites across varying sizes, compositions, isomers, and locations collectively contribute to overall activity due to their high intrinsic activity and abundance. The collectivity of active sites, despite their distinct local environments, configurations and reaction mechanisms, arises from their high intrinsic activity and considerable population. Data-driven machine learning reveals that this collectivity is governed by the balance between local atomic coordination and the adsorption energy. Using CO oxidation on Cu/CeO2 as an example, we validate the collective effect via agreement between computed mechanisms/kinetics and experimental data. This work provides insights into active sites in heterogeneous catalysis and highlights the potential of leveraging collective effects in cluster catalysis.

Date: 2025
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DOI: 10.1038/s41467-025-64187-3

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