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ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis
[Image: see text] Exploration of the chemical reaction space of chemical transformations in multicomponent mixtures is one of the main challenges in contemporary computational chemistry. To remove expert bias from mechanistic studies and to discover new chemistries, an automated graph-theoretical me...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753598/ https://www.ncbi.nlm.nih.gov/pubmed/36321652 http://dx.doi.org/10.1021/acs.jctc.2c00404 |
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author | Hashemi, Ali Bougueroua, Sana Gaigeot, Marie-Pierre Pidko, Evgeny A. |
author_facet | Hashemi, Ali Bougueroua, Sana Gaigeot, Marie-Pierre Pidko, Evgeny A. |
author_sort | Hashemi, Ali |
collection | PubMed |
description | [Image: see text] Exploration of the chemical reaction space of chemical transformations in multicomponent mixtures is one of the main challenges in contemporary computational chemistry. To remove expert bias from mechanistic studies and to discover new chemistries, an automated graph-theoretical methodology is proposed, which puts forward a network formalism of homogeneous catalysis reactions and utilizes a network analysis tool for mechanistic studies. The method can be used for analyzing trajectories with single and multiple catalytic species and can provide unique conformers of catalysts including multinuclear catalyst clusters along with other catalytic mixture components. The presented three-step approach has the integrated ability to handle multicomponent catalytic systems of arbitrary complexity (mixtures of reactants, catalyst precursors, ligands, additives, and solvents). It is not limited to predefined chemical rules, does not require prealignment of reaction mixture components consistent with a reaction coordinate, and is not agnostic to the chemical nature of transformations. Conformer exploration, reactive event identification, and reaction network analysis are the main steps taken for identifying the pathways in catalytic systems given the starting precatalytic reaction mixture as the input. Such a methodology allows us to efficiently explore catalytic systems in realistic conditions for either previously observed or completely unknown reactive events in the context of a network representing different intermediates. Our workflow for the catalytic reaction space exploration exclusively focuses on the identification of thermodynamically feasible conversion channels, representative of the (secondary) catalyst deactivation or inhibition paths, which are usually most difficult to anticipate based solely on expert chemical knowledge. Thus, the expert bias is sought to be removed at all steps, and the chemical intuition is limited to the choice of the thermodynamic constraint imposed by the applicable experimental conditions in terms of threshold energy values for allowed transformations. The capabilities of the proposed methodology have been tested by exploring the reactivity of Mn complexes relevant for catalytic hydrogenation chemistry to verify previously postulated activation mechanisms and unravel unexpected reaction channels relevant to rare deactivation events. |
format | Online Article Text |
id | pubmed-9753598 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-97535982022-12-16 ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis Hashemi, Ali Bougueroua, Sana Gaigeot, Marie-Pierre Pidko, Evgeny A. J Chem Theory Comput [Image: see text] Exploration of the chemical reaction space of chemical transformations in multicomponent mixtures is one of the main challenges in contemporary computational chemistry. To remove expert bias from mechanistic studies and to discover new chemistries, an automated graph-theoretical methodology is proposed, which puts forward a network formalism of homogeneous catalysis reactions and utilizes a network analysis tool for mechanistic studies. The method can be used for analyzing trajectories with single and multiple catalytic species and can provide unique conformers of catalysts including multinuclear catalyst clusters along with other catalytic mixture components. The presented three-step approach has the integrated ability to handle multicomponent catalytic systems of arbitrary complexity (mixtures of reactants, catalyst precursors, ligands, additives, and solvents). It is not limited to predefined chemical rules, does not require prealignment of reaction mixture components consistent with a reaction coordinate, and is not agnostic to the chemical nature of transformations. Conformer exploration, reactive event identification, and reaction network analysis are the main steps taken for identifying the pathways in catalytic systems given the starting precatalytic reaction mixture as the input. Such a methodology allows us to efficiently explore catalytic systems in realistic conditions for either previously observed or completely unknown reactive events in the context of a network representing different intermediates. Our workflow for the catalytic reaction space exploration exclusively focuses on the identification of thermodynamically feasible conversion channels, representative of the (secondary) catalyst deactivation or inhibition paths, which are usually most difficult to anticipate based solely on expert chemical knowledge. Thus, the expert bias is sought to be removed at all steps, and the chemical intuition is limited to the choice of the thermodynamic constraint imposed by the applicable experimental conditions in terms of threshold energy values for allowed transformations. The capabilities of the proposed methodology have been tested by exploring the reactivity of Mn complexes relevant for catalytic hydrogenation chemistry to verify previously postulated activation mechanisms and unravel unexpected reaction channels relevant to rare deactivation events. American Chemical Society 2022-11-02 2022-12-13 /pmc/articles/PMC9753598/ /pubmed/36321652 http://dx.doi.org/10.1021/acs.jctc.2c00404 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Hashemi, Ali Bougueroua, Sana Gaigeot, Marie-Pierre Pidko, Evgeny A. ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis |
title | ReNeGate: A Reaction
Network Graph-Theoretical Tool
for Automated Mechanistic Studies in Computational Homogeneous Catalysis |
title_full | ReNeGate: A Reaction
Network Graph-Theoretical Tool
for Automated Mechanistic Studies in Computational Homogeneous Catalysis |
title_fullStr | ReNeGate: A Reaction
Network Graph-Theoretical Tool
for Automated Mechanistic Studies in Computational Homogeneous Catalysis |
title_full_unstemmed | ReNeGate: A Reaction
Network Graph-Theoretical Tool
for Automated Mechanistic Studies in Computational Homogeneous Catalysis |
title_short | ReNeGate: A Reaction
Network Graph-Theoretical Tool
for Automated Mechanistic Studies in Computational Homogeneous Catalysis |
title_sort | renegate: a reaction
network graph-theoretical tool
for automated mechanistic studies in computational homogeneous catalysis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753598/ https://www.ncbi.nlm.nih.gov/pubmed/36321652 http://dx.doi.org/10.1021/acs.jctc.2c00404 |
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