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Metal-organic frameworks (MOFs) have been widely investigated for challengingcatalytic transformations due to their well-defined structures and high degreeof synthetic tunability. These features, at least in principle, make MOFsideally suited for a computational approach towards catalyst design anddiscovery. Nonetheless, the widespread use of data science and machine learningto accelerate the discovery of MOF catalysts has yet to be substantiallyrealized. In this review, we provide an overview of recent work that sets thestage for future high-throughput computational screening and machine learningstudies involving MOF catalysts. This is followed by a discussion of severalchallenges currently facing the broad adoption of data-centric approaches inMOF computational catalysis, and we share possible solutions that can helppropel the field forward.

Realizing the Data-Driven, Computational Discovery of Metal-Organic Framework Catalysts