TY - JOUR
T1 - Realizing the data-driven, computational discovery of metal-organic framework catalysts
AU - Rosen, Andrew S.
AU - Notestein, Justin M.
AU - Snurr, Randall Q.
N1 - Funding Information:
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).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/3
Y1 - 2022/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85119442220&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85119442220&partnerID=8YFLogxK
U2 - 10.1016/j.coche.2021.100760
DO - 10.1016/j.coche.2021.100760
M3 - Review article
AN - SCOPUS:85119442220
VL - 35
JO - Current Opinion in Chemical Engineering
JF - Current Opinion in Chemical Engineering
SN - 2211-3398
M1 - 100760
ER -