Dataset for Interpretable Machine Learning Reveals Structural Insights into Selective C–H Borylation by Metal-Organic Framework-Supported Ni Catalysts

Metal-organic frameworks (MOFs) offer a vast design space as tunable supports for catalytic applications, yet their structural complexity often obscures structure-activity relationships. This study investigates MOF-supported nickel (Ni) catalysts for selective sp³ and sp² C–H borylation. Using interpretable machine learning, we developed 45 concise and comprehensive descriptors that capture the diverse structural features of the vast MOF family, derived from over 470,000 MOF structures. These descriptors allowed us to identify key factors governing the sp³ vs. sp² selectivity. Our findings reveal that sp³ C–H borylation occurs within MOF pores via radical-mediated hydrogen atom transfer (HAT), while sp² C–H borylation is associated with surface or defect sites, favoring a concerted metalation-deprotonation (CMD) mechanism. Guided by these insights, we designed Ni catalysts achieving sp³ C–H selectivity of up to 97.8% and sp² C–H selectivity of up to 88.7%. This work provides a systematic framework for rational design of and transferable insights into MOF-supported catalysts.