Active phase discovery in heterogeneous catalysis via topology-guided sampling and machine learning

Shisheng Zheng (), Xi-Ming Zhang, Heng-Su Liu, Ge-Hao Liang, Si-Wang Zhang, Wentao Zhang, Bingxu Wang, Jingling Yang, Xian’an Jin, Feng Pan () and Jian-Feng Li ()
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Shisheng Zheng: Xiamen University
Xi-Ming Zhang: Xiamen University
Heng-Su Liu: Xiamen University
Ge-Hao Liang: Xiamen University
Si-Wang Zhang: Xiamen University
Wentao Zhang: Shenzhen Graduate School
Bingxu Wang: Shenzhen Graduate School
Jingling Yang: Xiamen University
Xian’an Jin: Xiamen University
Feng Pan: Shenzhen Graduate School
Jian-Feng Li: Xiamen University

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Understanding active phases across interfaces, interphases, and even within the bulk under varying external conditions and environmental species is critical for advancing heterogeneous catalysis. Describing these phases through computational models faces the challenges in the generation and calculation of a vast array of atomic configurations. Here, we present a framework for the automatic and efficient exploration of active phases. This approach utilizes a topology-based algorithm leveraging persistent homology to systematically sample configurations across diverse coordination environments and material morphologies. Simultaneously, efficient machine learning force fields enable rapid computations. We demonstrate the effectiveness of this framework in two systems: hydrogen absorption in Pd, where hydrogen penetrates subsurface layers and the bulk, inducing a “hex” reconstruction critical for CO2 electroreduction, explored through 50,000 sampled configurations; and the oxidation dynamics of Pt clusters, where oxygen incorporation renders the clusters less active during oxygen reduction reactions, investigated through 100,000 sampled configurations. In both cases, the predicted active phases and their impacts on catalytic mechanisms closely align with previous experimental observations, indicating that the proposed strategy can model complex catalytic systems and discovery active phases under specific environmental conditions.

Date: 2025
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DOI: 10.1038/s41467-025-57824-4

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