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Understanding chemical reactivity using the activation strain model

Abstract

Understanding chemical reactivity through the use of state-of-the-art computational techniques enables chemists to both predict reactivity and rationally design novel reactions. This protocol aims to provide chemists with the tools to implement a powerful and robust method for analyzing and understanding any chemical reaction using PyFrag 2019. The approach is based on the so-called activation strain model (ASM) of reactivity, which relates the relative energy of a molecular system to the sum of the energies required to distort the reactants into the geometries required to react plus the strength of their mutual interactions. Other available methods analyze only a stationary point on the potential energy surface, but our methodology analyzes the change in energy along a reaction coordinate. The use of this methodology has been proven to be critical to the understanding of reactions, spanning the realms of the inorganic and organic, as well as the supramolecular and biochemical, fields. This protocol provides step-by-step instructions—starting from the optimization of the stationary points and extending through calculation of the potential energy surface and analysis of the trend-decisive energy terms—that can serve as a guide for carrying out the analysis of any given reaction of interest within hours to days, depending on the size of the molecular system.

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Fig. 1: General workflow of the procedure.
Fig. 2: Examples of systems for which the ASM has been successfully applied.
Fig. 3: Activation strain diagram.
Fig. 4: Analysis of the oxidative insertion of palladium into the C–Cl bond of chloromethane.
Fig. 5: Comparison of the strain-promoted azide–alkyne cycloaddition reactivity between methyl azide and two different cycloalkynes.

Data availability

The data that support this study are available from the corresponding author upon reasonable request.

Code availability

The open-source PyFrag 2019 code that was used to generate all the data shown in the protocol can be found at https://github.com/sunxb05/PyFrag.

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Acknowledgements

We thank the Netherlands Organization for Scientific Research (NWO) and the Dutch Astrochemistry Network (DAN) for financial support. Furthermore, we thank X. Sun for fruitful discussions and for testing of the complete protocol.

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Authors

Contributions

P.V., S.C.C.v.d.L., and T.A.H. participated in the design of the protocol. P.V., S.C.C.v.d.L., C.F.G., F.M.B., and T.A.H. wrote the manuscript.

Corresponding authors

Correspondence to F. Matthias Bickelhaupt or Trevor A. Hamlin.

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The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Xin Hong and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Vermeeren, P., Sun, X. & Bickelhaupt, F. M. Sci. Rep. 8, 10729 (2018): https://doi.org/10.1038/s41598-018-28998-3

Sun, X., Soini, T. M., Poater, J., Hamlin, T. A. & Bickelhaupt, F. M. J. Comput. Chem. 40, 2227–2233 (2019): https://doi.org/10.1002/jcc.25871

Key data used in this protocol

Vermeeren, P., Sun, X. & Bickelhaupt, F. M. Sci. Rep. 8, 10729 (2018): https://doi.org/10.1038/s41598-018-28998-3

Hamlin, T. A., Levandowski, B. J., Narsaria, A. K., Houk, K. N. & F. Bickelhaupt, F. M. Chem. Eur. J. 25, 6342–6348 (2019): https://doi.org/10.1002/chem.201900295

Supplementary information

Supplementary Information

Supplementary Methods 1–17, Supplementary Data 1 and 2

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Vermeeren, P., van der Lubbe, S.C.C., Fonseca Guerra, C. et al. Understanding chemical reactivity using the activation strain model. Nat Protoc 15, 649–667 (2020). https://doi.org/10.1038/s41596-019-0265-0

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