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

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 sp 3 and sp 2 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 sp 3 versus sp 2 selectivity, revealing distinct activation mechanisms: sp 3 C─H borylation preferentially occurs within MOF cavities via a radical‐mediated hydrogen atom transfer (HAT) mechanism, whereas sp 2 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% sp 3 selectivity and 88.7% sp 2 selectivity. This work provides a systematic framework for rational catalyst design and establishes generalizable principles for controlling activity preference in MOF‐supported catalysis.