ExcelAutomat 1.3: Fragment analysis based on the distortion/interaction-activation strain model

ExcelAutomat 1.4: generation of supporting information

Quantum chemical computations generate output files with data. The processing of these data generates results which are presented in a target document, such as a manuscript or supporting information (SI). Several tools and techniques can be employed to facilitate the transfer of data which, otherwise, can be time-consuming with a large number of files. However, depending on the user’s technical knowledge or expertise with the software, additional time has to be invested to set up the software or use the tools. In addition, to the best of the authors’ knowledge, the tools currently available do not provide an option to transfer the data from the output files directly to the target document without the use of custom scripts. The ExcelAutomat tool (Laloo et al., J. Comput. Aided Mol. Des. 2017, 31, 667 and Laloo et al., J. Comp. Chem. 2019, 40, 3) is spreadsheet-based and was developed in-house to facilitate the steps involved in the processing of computational files. The tool was adapted to facilitate the generation of SI in an update of ExcelAutomat 1.4. A graphical user interface was designed where the options for the generation of SI can be defined. ExcelAutomat 1.4 is compatible with Microsoft Excel and the open-source LibreOffice Calc. The extensible tool supports various software packages and parameters by interfacing with the cclib library and through built-in codes. The tool provides a method to transfer data from output files directly to a Microsoft Word or LibreOffice Writer document and can reduce the number of steps, tools or technical knowledge needed to generate SI, especially for users who are familiar with Microsoft Excel or LibreOffice Calc.

Theoretical study of a derivative of chlorophosphine with aliphatic and aromatic Grignard reagents: SN2@P or the novel SN2@Cl followed by SN2@C?

The proposed SN2 reactions of a hindered organophosphorus reactant with aliphatic and aromatic nucleophiles [Ye et al., Org. Lett., 2017, 19, 5384–5387] were studied theoretically in order to explain the observed stereochemistry of the products.

Understanding chemical reactivity using the activation strain model

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.

Activation Strain Analyses of Counterion and Solvent Effects on the Ion-Pair SN 2 Reaction of NH 2 - and CH3 Cl

We have computationally studied the bimolecular nucleophilic substitution (S_N 2) reactions of M_n NH₂ ^(n-1) + CH₃ Cl (M⁺  = Li⁺ , Na⁺ , K⁺ , and MgCl⁺ ; n = 0, 1) in the gas phase and in tetrahydrofuran solution at OLYP/6-31++G(d,p) using polarizable continuum model implicit solvation. We wish to explore and understand the effect of the metal counterion M⁺ and of solvation on the reaction profile and the stereochemical preference, that is, backside (S_N 2-b) versus frontside attack (S_N 2-f). The results were compared to the corresponding ion-pair S_N 2 reactions involving F^- and OH^- nucleophiles. Our analyses with an extended activation strain model of chemical reactivity uncover and explain various trends in S_N 2 reactivity along the nucleophiles F^- , OH^- , and NH 2 - , including solvent and counterion effects. © 2019 Wiley Periodicals, Inc.

PyFrag 2019-Automating the exploration and analysis of reaction mechanisms

We present a substantial update to the PyFrag 2008 program, which was originally designed to perform a fragment-based activation strain analysis along a provided potential energy surface. The original PyFrag 2008 workflow facilitated the characterization of reaction mechanisms in terms of the intrinsic properties, such as strain and interaction, of the reactants. The new PyFrag 2019 program has automated and reduced the time-consuming and laborious task of setting up, running, analyzing, and visualizing computational data from reaction mechanism studies to a single job. PyFrag 2019 resolves three main challenges associated with the automated computational exploration of reaction mechanisms: it (1) computes the reaction path by carrying out multiple parallel calculations using initial coordinates provided by the user; (2) monitors the entire workflow process; and (3) tabulates and visualizes the final data in a clear way. The activation strain and canonical energy decomposition results that are generated relate the characteristics of the reaction profile in terms of intrinsic properties (strain, interaction, orbital overlaps, orbital energies, populations) of the reactant species. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.