4.8 Article

Promotion Energy Analysis Predicts Reaction Modes: Nucleophilic and Electrophilic Aromatic Substitution Reactions

期刊

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
卷 143, 期 11, 页码 4367-4378

出版社

AMER CHEMICAL SOC
DOI: 10.1021/jacs.1c00307

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资金

  1. Research Foundation-Flanders (FWO) [1203419N]
  2. Israel Science Foundation [ISF 520/18]

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This research develops an approach to predict major reaction modes associated with a chemical system based on reactant properties from the valence bond perspective. By analyzing promotion energies, predictive information about potential transition states and products can be obtained, and insight into the impact of environmental effects on mechanistic landscapes can be provided.
To develop an approach to pre-emptively predict the existence of major reaction modes associated with a chemical system, based on exclusive consideration of reactant properties, we build herein on the valence bond perspective of chemical reactivity. In this perspective, elementary chemical reactions are conceptualized as crossovers between individual diabatic/semilocalized states. As demonstrated, the spacings between the main diabatic states in the reactant geometries-the so-called promotion energies-contain predictive information about which types of crossings are likely to occur on a potential energy surface, facilitating the identification of potential transition states and products. As an added bonus, promotion energy analysis provides direct insight into the impact of environmental effects, e.g., the presence of (polar) solvents and/or (local) electric fields, on a mechanistic landscape. We illustrate the usefulness of our approach by focusing on model nucleophilic and electrophilic aromatic substitution reactions. Overall, we envision our analysis to be useful not only as a tool for conceptualizing individual mechanistic landscapes but also as a facilitator of systematic reaction-network exploration efforts. Because the emerging \TB descriptors are computationally inexpensive (and can alternatively be inferred through machine learning), they could be evaluated on-the-fly as part of an exploration algorithm. The so-predicted reaction modes could subsequently be examined in detail through computationally more-demanding methods.

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