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 language | English (US) |
|---|---|
| Article number | 100760 |
| Journal | Current Opinion in Chemical Engineering |
| Volume | 35 |
| DOIs | |
| State | Published - Mar 2022 |
Funding
A.S.R. was supported by a fellowship award through the National Defense Science and Engineering Graduate (NDSEG) Fellowship Program, sponsored by the Air Force Research Laboratory (AFRL), the Office of Naval Research (ONR) and the Army Research Office (ARO). A.S.R. also acknowledges support from a Ryan Fellowship through the International Institute for Nanotechnology as well a Presidential Fellowship through the Graduate School at Northwestern University . The material in this work is supported by the Institute for Catalysis in Energy Processes (ICEP) via the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences (award number DE-FG02-03ER15457).
ASJC Scopus subject areas
- General Energy