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

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Article number100760
JournalCurrent Opinion in Chemical Engineering
Volume35
DOIs
StatePublished - Mar 2022

ASJC Scopus subject areas

  • Energy(all)

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