Understanding chemical reactivity using the activation strain model.

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ID: 80279
2020
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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.
Reference Key
vermeeren2020understandingnature Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Vermeeren, Pascal;van der Lubbe, Stephanie C C;Fonseca Guerra, Célia;Bickelhaupt, F Matthias;Hamlin, Trevor A;
Journal nature protocols
Year 2020
DOI
10.1038/s41596-019-0265-0
URL
Keywords
photocatalyst l-cysteine polyethylene glycol mn-zns quantum dot organic dye zeolite nay

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