Volume 64, Issue 28 e202505931
Research Article

Machine Learning Reveals In-Cavity Versus Surface Activity for Selective C─H Borylation by Metal-Organic Framework Catalysts

Zhaomin Su

Zhaomin Su

iChem, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, 422 South Siming Rd., Siming District, Xiamen, Fujian, 361005 P.R. China

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Bingling Dai

Bingling Dai

iChem, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, 422 South Siming Rd., Siming District, Xiamen, Fujian, 361005 P.R. China

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Xue Wang

Xue Wang

iChem, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, 422 South Siming Rd., Siming District, Xiamen, Fujian, 361005 P.R. China

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Yibin Jiang

Corresponding Author

Yibin Jiang

Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiang'an South Road, Xiang'an District, Xiamen, Fujian, 361100 P.R. China

E-mail: [email protected]; [email protected]

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Wenbin Lin

Wenbin Lin

Department of Chemistry, The University of Chicago, 929 East 57th Street, Chicago, Illinois, 60637 USA

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Cheng Wang

Corresponding Author

Cheng Wang

iChem, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, 422 South Siming Rd., Siming District, Xiamen, Fujian, 361005 P.R. China

Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiang'an South Road, Xiang'an District, Xiamen, Fujian, 361100 P.R. China

E-mail: [email protected]; [email protected]

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First published: 06 May 2025
Citations: 1

Graphical Abstract

A set of 45 general descriptors was developed to capture the diverse structural features of a wide range of MOFs, enabling the study of MOF-supported nickel (Ni) catalysts for C─H borylation. This analysis revealed key factors that govern sp3 versus sp2 selectivity, allowing for the rational design of Ni catalysts with up to 97.8% sp3 selectivity and 88.7% sp2 selectivity.

Abstract

Metal-organic frameworks (MOFs) provide an expansive and tunable platform for heterogeneous catalysis, yet distinguishing between catalytic reactions occurring within their pores and those on their external surfaces remains a challenge. This study employs interpretable machine learning to elucidate structure-activity relationships in MOF-supported nickel (Ni) catalysts for selective sp3 and sp2 C─H borylation. By analyzing over 470 000 MOF structures, we developed a set of 45 concise and chemically meaningful descriptors that capture key structural variations across MOFs. These descriptors enabled us to identify the critical factors governing sp3 versus sp2 selectivity, revealing distinct activation mechanisms: sp3 C─H borylation preferentially occurs within MOF cavities via a radical-mediated hydrogen atom transfer (HAT) mechanism, whereas sp2 C─H borylation is associated with surface or defect sites, favoring a concerted metalation-deprotonation (CMD) pathway. Guided by these insights, we designed Ni catalysts that achieve up to 97.8% sp3 selectivity and 88.7% sp2 selectivity. This work provides a systematic framework for rational catalyst design and establishes generalizable principles for controlling activity preference in MOF-supported catalysis.

Conflict of Interests

The authors declare no conflict of interest.

Data Availability Statement

The authors declare that the data supporting the findings of this study are available within the article and its Supporting Information files. The raw data for the paper has been deposited in Figshare under accession code DOI link: https://doi.org/10.6084/m9.figshare.28342175.v2.

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