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 intrin...

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Bibliographic Details
Published in:Journal of computational chemistry Vol. 40; no. 25; pp. 2227 - 2233
Main Authors: Sun, Xiaobo, Soini, Thomas M., Poater, Jordi, Hamlin, Trevor A., Bickelhaupt, F. Matthias
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 30.09.2019
Wiley Subscription Services, Inc
Subjects:
activation strain model
Automation
Computational chemistry
Decomposition reactions
density functional calculations
energy decomposition analysis
Exploration
Organic chemistry
Potential energy
program
Reaction mechanisms
Software and Updates
Strain analysis
Workflow
energy decomposition analysis
reaction mechanisms
density functional calculations
automation
program
activation strain model
ISSN:0192-8651, 1096-987X, 1096-987X
Online Access:Get full text
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Summary: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. PyFrag 2019 is a user‐friendly python program that resolves three main challenges associated with the automatized computational exploration of reaction mechanisms: (1) the management of multiple parallel calculations to automatically find a reaction path; (2) the real‐time monitoring of the entire computational process; and (3) the analysis and presentation of these data in a clear and informative way to rationalize the characteristics of the reaction profile in terms of intrinsic properties of the reactant species by means of the activation strain model (ASM) and an energy decomposition analysis (EDA, when using ADF).
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ISSN:0192-8651
1096-987X
1096-987X
DOI:10.1002/jcc.25871