Interpretable Data‐Driven Descriptors for Establishing the Structure‐Activity Relationship of Metal–Organic Frameworks Toward Oxygen Evolution Reaction

The development of readily accessible and interpretable descriptors is pivotal yet challenging in the rational design of metal–organic framework (MOF) catalysts. This study presents a straightforward and physically interpretable activity descriptor for the oxygen evolution reaction (OER), derived from a dataset of bimetallic Ni‐based MOFs. Through an artificial‐intelligence (AI) data‐mining subgroup discovery (SGD) approach, a combination of the d‐band center and number of missing electrons in eg states of Ni, as well as the first ionization energy and number of electrons in eg states of the substituents, is revealed as a gene of a superior OER catalyst. The found descriptor, obtained from the AI analysis of a dataset of MOFs containing 3–5d transition metals and 13 organic linkers, has been demonstrated to facilitate in‐depth understanding of structure–activity relationship at the molecular orbital level. The descriptor is validated experimentally for 11 Ni‐based MOFs. Combining SGD with physical insights and experimental verification, our work offers a highly efficient approach for screening MOF‐based OER catalysts, simultaneously providing comprehensive understanding of the catalytic mechanism. We present a workflow that combines artificial‐intelligence data‐mining subgroup discovery method with density‐functional theory calculations to find descriptors of catalytic activity of Ni‐based MOFs in oxygen evolution reaction (OER). The identified data‐driven descriptors do not only guide the rational design of efficient MOF‐based catalysts, but also provide physical insights overarching existing knowledge on physical factors governing OER catalysis by MOFs.