Interpretable Data-Driven Descriptors for Establishing the Structure-Activity Relationship of Metal-Organic...

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_g states of Ni, as well as the first ionization energy and number of electrons in e_g 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.