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

期刊

CHEMICAL REVIEWS
卷 119, 期 11, 页码 6509-6560

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.chemrev.9b00073

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资金

  1. Research Corporation (Scialog Award)
  2. Institute for Basic Science in Korea [IBS-R010-A1]
  3. U.S. Department of Energy, Office of Science, Basic Energy Sciences, Catalysis Science Program [DE-SC0018329]
  4. National Research Foundation of Korea [IBS-R010-D1-2019-A00] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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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.

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