Identifying promising metal–organic frameworks for heterogeneous catalysis via high‐throughput periodic density functional theory.

Metal–organic frameworks (MOFs) are a class of nanoporous materials with highly tunable structures in terms of both chemical composition and topology. Due to their tunable nature, high‐throughput computational screening is a particularly appealing method to reduce the time‐to‐discovery of MOFs with desirable physical and chemical properties. In this work, a fully automated, high‐throughput periodic density functional theory (DFT) workflow for screening promising MOF candidates was developed and benchmarked, with a specific focus on applications in catalysis. As a proof‐of‐concept, we use the high‐throughput workflow to screen MOFs containing open metal sites (OMSs) from the Computation‐Ready, Experimental MOF database for the oxidative C—H bond activation of methane. The results from the screening process suggest that, despite the strong C—H bond strength of methane, the main challenge from a screening standpoint is the identification of MOFs with OMSs that can be readily oxidized at moderate reaction conditions. © 2019 Wiley Periodicals, Inc. In this work, a high‐throughput periodic density functional theory (DFT) workflow is developed for the automated screening of metal–organic frameworks for applications in catalysis. As a proof‐of‐concept, the DFT workflow is then used to screen MOFs with open metal sites from the Computation‐Ready, Experimental MOF database to identify promising candidates for methane activation. [ABSTRACT FROM AUTHOR]