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

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...

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Bibliographic Details
Published in:Journal of the American Chemical Society Vol. 143; no. 11; pp. 4367 - 4378
Main Authors: Stuyver, Thijs, Shaik, Sason
Format: Journal Article
Language:English
Published: United States American Chemical Society 24-03-2021
Online Access:Get full text
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Summary: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 VB 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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ISSN:0002-7863
1520-5126
DOI:10.1021/jacs.1c00307