  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.
