Realizing the data-driven, computational discovery of metal-organic framework catalysts

Rosen, Andrew S.; Notestein, Justin M.; Snurr, Randall Q.

doi:10.1016/j.coche.2021.100760

Realizing the data-driven, computational discovery of metal-organic framework catalysts

Journal Article · Mon Nov 22 00:00:00 EST 2021 · Current Opinion in Chemical Engineering

DOI:https://doi.org/10.1016/j.coche.2021.100760· OSTI ID:1870849

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Northwestern Univ., Evanston, IL (United States); Northwestern University
Northwestern Univ., Evanston, IL (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Northwestern Univ., Evanston, IL (United States)

Sponsoring Organization:: Air Force Research Laboratory (AFRL); Army Research Office (ARO); Office of Naval Research (ONR); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: FG02-03ER15457

OSTI ID:: 1870849

Journal Information:: Current Opinion in Chemical Engineering, Journal Name: Current Opinion in Chemical Engineering Vol. 35; ISSN 2211-3398

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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