Computational catalysis for metal-organic frameworks: An overview

Metal-organic frameworks (MOFs), a family of porous hybrid organic/inorganic materials, have shown great promise for many challenging chemical applications including gas separations, catalysis, and sensors. Numerous recent articles explore the field of catalytically active MOFs at an experimental and computational level. The stability, porosity, and periodic nature of MOFs allow them to act as both the active catalyst as well as supporting material to single atoms or small metal clusters. This review highlights recent work performed on catalytic reactions promoted by MOFs from a computational and theoretical standpoint. Computational modeling includes the elucidation of reaction mechanisms, the characterization of electronic structure effects of key intermediates and transition states, and the interpretation of experimental data. These topics are covered in this review article, and examples from the recent literature are presented. Studies that aim to better understand structure-function relations such as descriptors of catalytic activity have the potential to contribute to the design of new MOF catalysts are also included. We believe that the future of computational MOF catalysis will involve the use of high-throughput virtual screening and machine learning methods, some aspects of which are discussed in the current review. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Metal-organic frameworks (MOFs) show great promise in the field of catalysis, acting as both active catalysts and supporting materials. Recent research focuses on computational and theoretical aspects of catalytic reactions, including elucidating reaction mechanisms, characterizing electronic structure effects, and interpreting experimental data. Studies aim to understand structure-function relations and design new MOF catalysts, with future prospects involving high-throughput virtual screening and machine learning methods.