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 e