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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The data that support this study are available from the corresponding author upon reasonable request.
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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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.
The authors declare no competing interests.
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.
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 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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