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

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.金属有机框架（Metal-organic frameworks, MOFs）作为可调控的催化载体拥有广阔的设计空间，但其结构复杂性往往会掩盖构效关系。本研究针对用于选择性sp³与sp²碳氢键（C–H）硼化反应的MOF负载镍（Ni）催化剂展开探究。研究人员基于超过47万个MOF结构，利用可解释机器学习方法开发了45个简洁且全面的描述符，用以捕捉庞大MOF家族的多样结构特征。借助这些描述符，我们明确了决定sp³与sp²反应选择性的关键因素。研究结果显示，sp³ C–H硼化反应通过自由基介导的氢原子转移（radical-mediated hydrogen atom transfer, HAT）机制在MOF孔道内发生，而sp² C–H硼化反应则与催化剂表面或缺陷位点相关，倾向于采用协同金属化去质子化（concerted metalation-deprotonation, CMD）机制。基于上述发现设计的Ni催化剂，其sp³ C–H选择性最高可达97.8%，sp² C–H选择性最高可达88.7%。本研究为MOF负载催化剂的理性设计提供了系统性框架，同时也提供了可迁移的研究洞见。