Design and Optimization of Catalysts Based on Mechanistic Insights Derived from Quantum Chemical Reaction Modeling

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Title
Design and Optimization of Catalysts Based on Mechanistic Insights Derived from Quantum Chemical Reaction Modeling
Author(s)
Seihwan Ahn; Mannkyu Hong; Mahesh Sundararajan; Daniel H. Ess; Mu-Hyun Baik
Publication Date
2019-06
Journal
CHEMICAL REVIEWS, v.119, no.11, pp.6509 - 6560
Publisher
AMER CHEMICAL SOC
Abstract
© 2019 American Chemical Society.Until recently, computational tools were mainly used to explain chemical reactions after experimental results were obtained. With the rapid development of software and hardware technologies to make computational modeling tools more reliable, they can now provide valuable insights and even become predictive. In this review, we highlighted several studies involving computational predictions of unexpected reactivities or providing mechanistic insights for organic and organometallic reactions that led to improved experimental results. Key to these successful applications is an integration between theory and experiment that allows for incorporation of empirical knowledge with precise computed values. Computer modeling of chemical reactions is already a standard tool that is being embraced by an ever increasing group of researchers, and it is clear that its utility in predictive reaction design will increase further in the near future
URI
https://pr.ibs.re.kr/handle/8788114/6071
ISSN
0009-2665
Appears in Collections:
Center for Catalytic Hydrocarbon Functionalizations(분자활성 촉매반응 연구단) > Journal Papers (저널논문)
Files in This Item:
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