Realizing the data-driven, computational discovery of metal-organic framework catalysts. (March 2022)

• Record Type: Journal Article
• Authors: Rosen, Andrew S; Notestein, Justin M; Snurr, Randall Q
• Is Part Of: Current opinion in chemical engineering. Volume 35(2022)
• Publication Date: 2022-03
• Subjects: Chemical engineering -- Periodicals; Chemical engineering; Periodicals; 660.05
• DOI: 10.1016/j.coche.2021.100760
• Languages: English
• ISSNs: 2211-3398

Abstract

Graphical abstract: Highlights: Big data approaches are needed to efficiently explore MOF catalyst space. High-throughput protocols have been developed to screen large MOF datasets. Machine learning is poised to accelerate MOF catalyst discovery. While challenges remain, there are many recent developments and new opportunities. Abstract : Metal-organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally suited for a computational approach towards catalyst design and discovery. Nonetheless, the widespread use of data science and machine learning to accelerate the discovery of MOF catalysts has yet to be substantially realized. In this review, we provide an overview of recent work that sets the stage for future high-throughput computational screening and machine learning studies involving MOF catalysts. This is followed by a discussion of several challenges currently facing the broad adoption of data-centric approaches in MOF computational catalysis, and we share possible solutions that can help propel the field forward.