1
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van Gerwen P, Briling KR, Calvino Alonso Y, Franke M, Corminboeuf C. Benchmarking machine-readable vectors of chemical reactions on computed activation barriers. DIGITAL DISCOVERY 2024; 3:932-943. [PMID: 38756222 PMCID: PMC11094696 DOI: 10.1039/d3dd00175j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/28/2024] [Indexed: 05/18/2024]
Abstract
In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well. Here, we bring together all of these efforts and benchmark various methods (Morgan fingerprints, the DRFP, the CGR representation-based Chemprop, SLATMd, B2Rl2, EquiReact and language model BERT + RXNFP) for the prediction of computed activation barriers on three diverse datasets.
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Affiliation(s)
- Puck van Gerwen
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Ksenia R Briling
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Yannick Calvino Alonso
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Malte Franke
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Clemence Corminboeuf
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
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2
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Martí C, Devereux C, Najm HN, Zádor J. Evaluation of Rate Coefficients in the Gas Phase Using Machine-Learned Potentials. J Phys Chem A 2024. [PMID: 38427974 DOI: 10.1021/acs.jpca.3c07872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
We assess the capability of machine-learned potentials to compute rate coefficients by training a neural network (NN) model and applying it to describe the chemical landscape on the C5H5 potential energy surface, which is relevant to molecular weight growth in combustion and interstellar media. We coupled the resulting NN with an automated kinetics workflow code, KinBot, to perform all necessary calculations to compute the rate coefficients. The NN is benchmarked exhaustively by evaluating its performance at the various stages of the kinetics calculations: from the electronic energy through the computation of zero point energy, barrier heights, entropic contributions, the portion of the PES explored, and finally the overall rate coefficients as formulated by transition state theory.
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Affiliation(s)
- Carles Martí
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551, United States
| | - Christian Devereux
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551, United States
| | - Habib N Najm
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551, United States
| | - Judit Zádor
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551, United States
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3
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Kalikadien AV, Mirza A, Hossaini AN, Sreenithya A, Pidko EA. Paving the road towards automated homogeneous catalyst design. Chempluschem 2024:e202300702. [PMID: 38279609 DOI: 10.1002/cplu.202300702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/20/2023] [Indexed: 01/28/2024]
Abstract
In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.
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Affiliation(s)
- Adarsh V Kalikadien
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Adrian Mirza
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Aydin Najl Hossaini
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Avadakkam Sreenithya
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Evgeny A Pidko
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
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4
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Levine DS, Jacobson LD, Bochevarov AD. Large Computational Survey of Intrinsic Reactivity of Aromatic Carbon Atoms with Respect to a Model Aldehyde Oxidase. J Chem Theory Comput 2023; 19:9302-9317. [PMID: 38085599 DOI: 10.1021/acs.jctc.3c00913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Aldehyde oxidase (AOX) and other related molybdenum-containing enzymes are known to oxidize the C-H bonds of aromatic rings. This process contributes to the metabolism of pharmaceutical compounds and, therefore, is of vital importance to drug pharmacokinetics. The present work describes an automated computational workflow and its use for the prediction of intrinsic reactivity of small aromatic molecules toward a minimal model of the active site of AOX. The workflow is based on quantum chemical transition state searches for the underlying single-step oxidation reaction, where the automated protocol includes identification of unique aromatic C-H bonds, creation of three-dimensional reactant and product complex geometries via a templating approach, search for a transition state, and validation of reaction end points. Conformational search on the reactants, products, and the transition states is performed. The automated procedure has been validated on previously reported transition state barriers and was used to evaluate the intrinsic reactivity of nearly three hundred heterocycles commonly found in approved drug molecules. The intrinsic reactivity of more than 1000 individual aromatic carbon sites is reported. Stereochemical and conformational aspects of the oxidation reaction, which have not been discussed in previous studies, are shown to play important roles in accurate modeling of the oxidation reaction. Observations on structural trends that determine the reactivity are provided and rationalized.
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Affiliation(s)
- Daniel S Levine
- Schrödinger, Inc., 1540 Broadway, Floor 24, New York, New York 10036, United States
| | - Leif D Jacobson
- Schrödinger, Inc., 101 SW Main Street, Suite 1300, Portland, Oregon 97204, United States
| | - Art D Bochevarov
- Schrödinger, Inc., 1540 Broadway, Floor 24, New York, New York 10036, United States
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5
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Johnson MS, Gierada M, Hermes ED, Bross DH, Sargsyan K, Najm HN, Zádor J. Pynta─An Automated Workflow for Calculation of Surface and Gas-Surface Kinetics. J Chem Inf Model 2023; 63:5153-5168. [PMID: 37559203 DOI: 10.1021/acs.jcim.3c00948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
Many important industrial processes rely on heterogeneous catalytic systems. However, given all possible catalysts and conditions of interest, it is impractical to optimize most systems experimentally. Automatically generated microkinetic models can be used to efficiently consider many catalysts and conditions. However, these microkinetic models require accurate estimation of many thermochemical and kinetic parameters. Manually calculating these parameters is tedious and error prone, involving many interconnected computations. We present Pynta, a workflow software for automating the calculation of surface and gas-surface reactions. Pynta takes the reactants, products, and atom maps for the reactions of interest, generates sets of initial guesses for all species and saddle points, runs all optimizations, frequency, and IRC calculations, and computes the associated thermochemistry and rate coefficients. It is able to consider all unique adsorption configurations for both adsorbates and saddle points, allowing it to handle high index surfaces and bidentate species. Pynta implements a new saddle point guess generation method called harmonically forced saddle point searching (HFSP). HFSP defines harmonic potentials based on the optimized adsorbate geometries and which bonds are breaking and forming that allow initial placements to be optimized using the GFN1-xTB semiempirical method to create reliable saddle point guesses. This method is reaction class agnostic and fast, allowing Pynta to consider all possible adsorbate site placements efficiently. We demonstrate Pynta on 11 diverse reactions involving monodenate, bidentate, and gas-phase species, many distinct reaction classes, and both a low and a high index facet of Cu. Our results suggest that it is very important to consider reactions between adsorbates adsorbed in all unique configurations for interadsorbate group transfers and reactions on high index surfaces.
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Affiliation(s)
- Matthew S Johnson
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
| | - Maciej Gierada
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
| | - Eric D Hermes
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
| | - David H Bross
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Khachik Sargsyan
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
| | - Habib N Najm
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
| | - Judit Zádor
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
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6
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Kraka E, Antonio JJ, Freindorf M. Reaction mechanism - explored with the unified reaction valley approach. Chem Commun (Camb) 2023; 59:7151-7165. [PMID: 37233449 DOI: 10.1039/d3cc01576a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
One of the ultimate goals of chemistry is to understand and manipulate chemical reactions, which implies the ability to monitor the reaction and its underlying mechanism at an atomic scale. In this article, we introduce the Unified Reaction Valley Approach (URVA) as a tool for elucidating reaction mechanisms, complementing existing computational procedures. URVA combines the concept of the potential energy surface with vibrational spectroscopy and describes a chemical reaction via the reaction path and the surrounding reaction valley traced out by the reacting species on the potential energy surface on their way from the entrance to the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting species is registered by a change in the normal vibrational modes spanning the reaction valley and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction, with curvature minima reflecting minimal change and curvature maxima indicating the location of important chemical events such as bond breaking/formation, charge polarization and transfer, rehybridization, etc. A decomposition of the path curvature into internal coordinate components or other coordinates of relevance for the reaction under consideration, provides comprehensive insight into the origin of the chemical changes taking place. After giving an overview of current experimental and computational efforts to gain insight into the mechanism of a chemical reaction and presenting the theoretical background of URVA, we illustrate how URVA works for three diverse processes, (i) [1,3] hydrogen transfer reactions; (ii) α-keto-amino inhibitor for SARS-CoV-2 Mpro; (iii) Rh-catalyzed cyanation. We hope that this article will inspire our computational colleagues to add URVA to their repertoire and will serve as an incubator for new reaction mechanisms to be studied in collaboration with our experimental experts in the field.
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Affiliation(s)
- Elfi Kraka
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, TX 75275-0314, USA.
| | - Juliana J Antonio
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, TX 75275-0314, USA.
| | - Marek Freindorf
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, TX 75275-0314, USA.
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7
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Zádor J, Martí C, Van de Vijver R, Johansen SL, Yang Y, Michelsen HA, Najm HN. Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces. J Phys Chem A 2023; 127:565-588. [PMID: 36607817 DOI: 10.1021/acs.jpca.2c06558] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Automation of rate-coefficient calculations for gas-phase organic species became possible in recent years and has transformed how we explore these complicated systems computationally. Kinetics workflow tools bring rigor and speed and eliminate a large fraction of manual labor and related error sources. In this paper we give an overview of this quickly evolving field and illustrate, through five detailed examples, the capabilities of our own automated tool, KinBot. We bring examples from combustion and atmospheric chemistry of C-, H-, O-, and N-atom-containing species that are relevant to molecular weight growth and autoxidation processes. The examples shed light on the capabilities of automation and also highlight particular challenges associated with the various chemical systems that need to be addressed in future work.
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Affiliation(s)
- Judit Zádor
- Combustion Research Facility, Sandia National Laboratories, Livermore94550, California, United States
| | - Carles Martí
- Combustion Research Facility, Sandia National Laboratories, Livermore94550, California, United States
| | | | - Sommer L Johansen
- Combustion Research Facility, Sandia National Laboratories, Livermore94550, California, United States
| | - Yoona Yang
- Combustion Research Facility, Sandia National Laboratories, Livermore94550, California, United States
| | - Hope A Michelsen
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder80309, Colorado, United States
| | - Habib N Najm
- Combustion Research Facility, Sandia National Laboratories, Livermore94550, California, United States
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8
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Krep L, Schmalz F, Solbach F, Komissarov L, Nevolianis T, Kopp WA, Verstraelen T, Leonhard K. A Reactive Molecular Dynamics Study of Chlorinated Organic Compounds. Part II: A ChemTraYzer Study of Chlorinated Dibenzofuran Formation and Decomposition Processes. Chemphyschem 2022; 24:e202200783. [PMID: 36511423 DOI: 10.1002/cphc.202200783] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 12/14/2022]
Abstract
In our two-paper series, we first present the development of ReaxFF CHOCl parameters using the recently published ParAMS parametrization tool. In this second part, we update the reactive Molecular Dynamics - Quantum Mechanics coupling scheme ChemTraYzer and combine it with our new ReaxFF parameters from Part I to study formation and decomposition processes of chlorinated dibenzofurans. We introduce a self-learning method for recovering failed transition-state searches that improves the overall ChemTraYzer transition-state search success rate by 10 percentage points to a total of 48 %. With ChemTraYzer, we automatically find and quantify more than 500 reactions using transition state theory and DFT. Among the discovered chlorinated dibenzofuran reactions are numerous reactions that are new to the literature. In three case studies, we discuss the set of reactions that are most relevant to the dibenzofuran literature: (i) bimolecular reactions of the chlorinated-dibenzofuran precursors phenoxy radical and 1,3,5-trichlorobenzene, (ii) dibenzofuran chlorination and pyrolysis, and (iii) oxidation of chlorinated dibenzofurans.
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Affiliation(s)
- Lukas Krep
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Felix Schmalz
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Florian Solbach
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Leonid Komissarov
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, B-9052, Ghent, Belgium
| | - Thomas Nevolianis
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Wassja A Kopp
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Toon Verstraelen
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, B-9052, Ghent, Belgium
| | - Kai Leonhard
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
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9
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Soroudi S, Kassaee MZ.
CO
2
trapping of selected
N
‐heterocyclic vinylidenes with an
NBO
mechanistic scrutiny by
DFT. J CHIN CHEM SOC-TAIP 2022. [DOI: 10.1002/jccs.202200041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Shadi Soroudi
- Department of Chemistry Tarbiat Modares University Tehran Iran
| | - Mohamad Zaman Kassaee
- Department of Chemistry Tarbiat Modares University Tehran Iran
- Pure and Applied Research LLC Nashville Tennessee USA
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10
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Krep L, Roy IS, Kopp W, Schmalz F, Huang C, Leonhard K. Efficient Reaction Space Exploration with ChemTraYzer-TAD. J Chem Inf Model 2022; 62:890-902. [PMID: 35142513 DOI: 10.1021/acs.jcim.1c01197] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The development of a reaction model is often a time-consuming process, especially if unknown reactions have to be found and quantified. To alleviate the reaction modeling process, automated procedures for reaction space exploration are highly desired. We present ChemTraYzer-TAD, a new reactive molecular dynamics acceleration technique aimed at efficient reaction space exploration. The new method is based on the basin confinement strategy known from the temperature-accelerated dynamics (TAD) acceleration method. Our method features integrated ChemTraYzer bond-order processing steps for the automatic and on-the-fly determination of the positions of virtual walls in configuration space that confine the system in a potential energy basin. We use the example of 1,3-dioxolane-4-hydroperoxide-2-yl radical oxidation to show that ChemTraYzer-TAD finds more than 100 different parallel reactions for the given set of reactants in less than 2 ns of simulation time. Among the many observed reactions, ChemTraYzer-TAD finds the expected typical low-temperature reactions despite the use of extremely high simulation temperatures up to 5000 K. Our method also finds a new concerted β-scission plus O2 addition with a lower reaction barrier than the literature-known and so-far dominant β-scission.
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Affiliation(s)
- Lukas Krep
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
| | - Indu Sekhar Roy
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
| | - Wassja Kopp
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
| | - Felix Schmalz
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
| | - Can Huang
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
| | - Kai Leonhard
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
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11
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Soroudi S, Kassaee MZ. Capture of CO
2
by novel diiodo‐
N
,
N
‐imidazoliumvinylidene: A theoretical quest. J PHYS ORG CHEM 2022. [DOI: 10.1002/poc.4323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Shadi Soroudi
- Department of Chemistry Tarbiat Modares University Tehran Iran
| | - Mohamad Zaman Kassaee
- Department of Chemistry Tarbiat Modares University Tehran Iran
- Pure and Applied Research LLC Nashville Tennessee USA
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12
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Ferro-Costas D, Cordeiro MNDS, Fernández-Ramos A. An integrated protocol to study hydrogen abstraction reactions by atomic hydrogen in flexible molecules: application to butanol isomers. Phys Chem Chem Phys 2022; 24:3043-3058. [DOI: 10.1039/d1cp03928h] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
This work presents a protocol designed to study hydrogen abstraction reactions by atomic hydrogen in molecules with multiple conformations.
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Affiliation(s)
- David Ferro-Costas
- Center for Research in Biological Chemistry and Molecular Materials (CIQUS), University of Santiago de Compostela, 15782, Santiago de Compostela, Spain
| | - M. Natália D. S. Cordeiro
- LAQV@REQUIMTE, Department of Chemistry & Biochemistry, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007, Porto, Portugal
| | - Antonio Fernández-Ramos
- Center for Research in Biological Chemistry and Molecular Materials (CIQUS), University of Santiago de Compostela, 15782, Santiago de Compostela, Spain
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13
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Klippenstein SJ. Spiers Memorial Lecture: theory of unimolecular reactions. Faraday Discuss 2022; 238:11-67. [DOI: 10.1039/d2fd00125j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
One hundred years ago, at an earlier Faraday Discussion meeting, Lindemann presented a mechanism that provides the foundation for contemplating the pressure dependence of unimolecular reactions. Since that time, our...
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14
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Heid E, Green WH. Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction. J Chem Inf Model 2021; 62:2101-2110. [PMID: 34734699 PMCID: PMC9092344 DOI: 10.1021/acs.jcim.1c00975] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
The estimation of
chemical reaction properties such as activation
energies, rates, or yields is a central topic of computational chemistry.
In contrast to molecular properties, where machine learning approaches
such as graph convolutional neural networks (GCNNs) have excelled
for a wide variety of tasks, no general and transferable adaptations
of GCNNs for reactions have been developed yet. We therefore combined
a popular cheminformatics reaction representation, the so-called condensed
graph of reaction (CGR), with a recent GCNN architecture to arrive
at a versatile, robust, and compact deep learning model. The CGR is
a superposition of the reactant and product graphs of a chemical reaction
and thus an ideal input for graph-based machine learning approaches.
The model learns to create a data-driven, task-dependent reaction
embedding that does not rely on expert knowledge, similar to current
molecular GCNNs. Our approach outperforms current state-of-the-art
models in accuracy, is applicable even to imbalanced reactions, and
possesses excellent predictive capabilities for diverse target properties,
such as activation energies, reaction enthalpies, rate constants,
yields, or reaction classes. We furthermore curated a large set of
atom-mapped reactions along with their target properties, which can
serve as benchmark data sets for future work. All data sets and the
developed reaction GCNN model are available online, free of charge,
and open source.
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Affiliation(s)
- Esther Heid
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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15
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Farina DS, Sirumalla SK, Mazeau EJ, West RH. Extensive High-Accuracy Thermochemistry and Group Additivity Values for Halocarbon Combustion Modeling. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c03076] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- David S. Farina
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Sai Krishna Sirumalla
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Emily J. Mazeau
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Richard H. West
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
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16
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Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States. Top Catal 2021. [DOI: 10.1007/s11244-021-01506-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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17
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Martínez-Núñez E, Barnes GL, Glowacki DR, Kopec S, Peláez D, Rodríguez A, Rodríguez-Fernández R, Shannon RJ, Stewart JJP, Tahoces PG, Vazquez SA. AutoMeKin2021: An open-source program for automated reaction discovery. J Comput Chem 2021; 42:2036-2048. [PMID: 34387374 DOI: 10.1002/jcc.26734] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/16/2021] [Accepted: 07/27/2021] [Indexed: 01/10/2023]
Abstract
AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (J. Comput. Chem. 2018, 39, 1922). This release features a number of new capabilities: rare-event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond-order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at: https://rxnkin.usc.es/index.php/AutoMeKin.
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Affiliation(s)
- Emilio Martínez-Núñez
- Department of Physical Chemistry, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - George L Barnes
- Department of Chemistry and Biochemistry, Siena College, Loudonville, New York, USA
| | - David R Glowacki
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK
| | - Sabine Kopec
- Institut de Sciences Moléculaires d'Orsay, UMR 8214, Université Paris-Sud - Université Paris-Saclay, Orsay, France
| | - Daniel Peláez
- Institut de Sciences Moléculaires d'Orsay, UMR 8214, Université Paris-Sud - Université Paris-Saclay, Orsay, France
| | - Aurelio Rodríguez
- Galicia Supercomputing Center (CESGA), Santiago de Compostela, Spain
| | | | - Robin J Shannon
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK
| | | | - Pablo G Tahoces
- Department of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Saulo A Vazquez
- Department of Physical Chemistry, University of Santiago de Compostela, Santiago de Compostela, Spain
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18
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Makoś MZ, Verma N, Larson EC, Freindorf M, Kraka E. Generative adversarial networks for transition state geometry prediction. J Chem Phys 2021; 155:024116. [PMID: 34266275 DOI: 10.1063/5.0055094] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
This work introduces a novel application of generative adversarial networks (GANs) for the prediction of starting geometries in transition state (TS) searches based on the geometries of reactants and products. The multi-dimensional potential energy space of a chemical reaction often complicates the location of a starting TS geometry, leading to the correct TS combining reactants and products in question. The proposed TS-GAN efficiently maps the space between reactants and products and generates reliable TS guess geometries, and it can be easily combined with any quantum chemical software package performing geometry optimizations. The TS-GAN was trained and applied to generate TS guess structures for typical chemical reactions, such as hydrogen migration, isomerization, and transition metal-catalyzed reactions. The performance of the TS-GAN was directly compared to that of classical approaches, proving its high accuracy and efficiency. The current TS-GAN can be extended to any dataset that contains sufficient chemical reactions for training. The software is freely available for training, experimentation, and prediction at https://github.com/ekraka/TS-GAN.
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Affiliation(s)
- Małgorzata Z Makoś
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA
| | - Niraj Verma
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA
| | - Eric C Larson
- Computer Science Department, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA
| | - Marek Freindorf
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA
| | - Elfi Kraka
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA
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19
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Fahim AM, Mohamed A, Ibrahim MA. Experimental and theoretical studies of some propiolate esters derivatives. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2021.130281] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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20
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Gong S, Wang Y, Tian Y, Wang L, Liu G. Rapid enthalpy prediction of transition states using molecular graph convolutional network. AIChE J 2021. [DOI: 10.1002/aic.17269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Siyuan Gong
- Key Laboratory for Green Chemical Technology of Ministry of Education, School of Chemical Engineering and Technology Tianjin University Tianjin China
| | - Yutong Wang
- Key Laboratory for Green Chemical Technology of Ministry of Education, School of Chemical Engineering and Technology Tianjin University Tianjin China
| | - Yajie Tian
- Key Laboratory for Green Chemical Technology of Ministry of Education, School of Chemical Engineering and Technology Tianjin University Tianjin China
- Henan Engineering Research Center of Resource and Energy Recovery from Waste, College of Chemistry and Chemical Engineering Henan University Kaifeng China
| | - Li Wang
- Key Laboratory for Green Chemical Technology of Ministry of Education, School of Chemical Engineering and Technology Tianjin University Tianjin China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Tianjin China
| | - Guozhu Liu
- Key Laboratory for Green Chemical Technology of Ministry of Education, School of Chemical Engineering and Technology Tianjin University Tianjin China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Tianjin China
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21
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Bai J, Geeson R, Farazi F, Mosbach S, Akroyd J, Bringley EJ, Kraft M. Automated Calibration of a Poly(oxymethylene) Dimethyl Ether Oxidation Mechanism Using the Knowledge Graph Technology. J Chem Inf Model 2021; 61:1701-1717. [PMID: 33825473 PMCID: PMC8154252 DOI: 10.1021/acs.jcim.0c01322] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
In
this paper, we develop a knowledge graph-based framework for
the automated calibration of combustion reaction mechanisms and demonstrate
its effectiveness on a case study of poly(oxymethylene)dimethyl ether
(PODEn, where n = 3)
oxidation. We develop an ontological representation for combustion
experiments, OntoChemExp, that allows for the semantic enrichment
of experiments within the J-Park simulator (JPS, theworldavatar.com), an
existing cross-domain knowledge graph. OntoChemExp is fully capable
of supporting experimental results in the Process Informatics Model
(PrIMe) database. Following this, a set of software agents are developed
to perform experimental result retrieval, sensitivity analysis, and
calibration tasks. The sensitivity analysis agent is used for both
generic sensitivity analyses and reaction selection for subsequent
calibration. The calibration process is performed as a sampling task,
followed by an optimization task. The agents are designed for use
with generic models but are demonstrated with ignition delay time
and laminar flame speed simulations. We find that calibration times
are reduced, while accuracy is increased compared to manual calibration,
achieving a 79% decrease in the objective function value, as defined
in this study. Further, we demonstrate how this workflow is implemented
as an extension of the JPS.
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Affiliation(s)
- Jiaru Bai
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K
| | - Rory Geeson
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.,Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, U.K
| | - Feroz Farazi
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K
| | - Sebastian Mosbach
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.,Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, Singapore 138602, Singapore
| | - Jethro Akroyd
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.,Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, Singapore 138602, Singapore
| | - Eric J Bringley
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K
| | - Markus Kraft
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.,Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, Singapore 138602, Singapore.,School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
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22
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Pattanaik L, Ingraham JB, Grambow CA, Green WH. Generating transition states of isomerization reactions with deep learning. Phys Chem Chem Phys 2020; 22:23618-23626. [PMID: 33112304 DOI: 10.1039/d0cp04670a] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Lack of quality data and difficulty generating these data hinder quantitative understanding of reaction kinetics. Specifically, conventional methods to generate transition state structures are deficient in speed, accuracy, or scope. We describe a novel method to generate three-dimensional transition state structures for isomerization reactions using reactant and product geometries. Our approach relies on a graph neural network to predict the transition state distance matrix and a least squares optimization to reconstruct the coordinates based on which entries of the distance matrix the model perceives to be important. We feed the structures generated by our algorithm through a rigorous quantum mechanics workflow to ensure the predicted transition state corresponds to the ground truth reactant and product. In both generating viable geometries and predicting accurate transition states, our method achieves excellent results. We envision workflows like this, which combine neural networks and quantum chemistry calculations, will become the preferred methods for computing chemical reactions.
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Affiliation(s)
- Lagnajit Pattanaik
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - John B Ingraham
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Colin A Grambow
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
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23
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Rasmussen MH, Jensen JH. Fast and automatic estimation of transition state structures using tight binding quantum chemical calculations. PEERJ PHYSICAL CHEMISTRY 2020. [DOI: 10.7717/peerj-pchem.15] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
We present a method for the automatic determination of transition states (TSs) that is based on Grimme’s RMSD-PP semiempirical tight binding reaction path method (J. Chem. Theory Comput. 2019, 15, 2847–2862), where the maximum energy structure along the path serves as an initial guess for DFT TS searches. The method is tested on 100 elementary reactions and located a total of 89 TSs correctly. Of the 11 remaining reactions, nine are shown not to be elementary reactions after all and for one of the two true failures the problem is shown to be the semiempirical tight binding model itself. Furthermore, we show that the GFN2-xTB RMSD-PP barrier is a good approximation for the corresponding DFT barrier for reactions with DFT barrier heights up to about 30 kcal/mol. Thus, GFN2-xTB RMSD-PP barrier heights, which can be estimated at the cost of a single energy minimisation, can be used to quickly identify reactions with low barriers, although it will also produce some false positives.
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Affiliation(s)
| | - Jan H. Jensen
- Department of Chemistry, University of Copenhagen, Copenhagen, Denmark
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24
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Affiliation(s)
- Evan Komp
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Stéphanie Valleau
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
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25
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Yang J, Wang Z, Lv G, Liu W, Wang Y, Sun X, Gao J. Indirect photodegradation of fludioxonil by hydroxyl radical and singlet oxygen in aquatic environment: Mechanism, photoproducts formation and eco-toxicity assessment. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2020; 197:110644. [PMID: 32325330 DOI: 10.1016/j.ecoenv.2020.110644] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 04/12/2020] [Accepted: 04/13/2020] [Indexed: 06/11/2023]
Abstract
Fludioxonil has been proven valuable as a broad-spectrum fungicide. However, there are concerns about its risk posed to non-target organisms in aquatic environments. In this paper, the mechanism, photoproducts transformation and eco-toxicity of fludioxonil during •OH/1O2-initiated process were systematically studied using quantum chemistry and computational toxicology. The results indicate that the two favorable pathways of •OH/1O2-initiated reactions are both occurred in pyrrole ring. It can conclude that the rate constants of •OH and 1O2 are 1.23 × 1010 and 3.69 × 107 M-1 s-1 at 298K, respectively, which results in half-lives of <2 days in surface waters under sunlit near-surface conditions. Based on toxicity assessments, these photoproducts showed a decreased aquatic toxicity but the majority products are still toxic. This study gives more insight into the chemical transformation mechanism of fludioxonil in aquatic environments.
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Affiliation(s)
- Jiaoxue Yang
- Environment Research Institute, Shandong University, Qingdao, 266237, China
| | - Zehua Wang
- Environment Research Institute, Shandong University, Qingdao, 266237, China
| | - Guochun Lv
- Environment Research Institute, Shandong University, Qingdao, 266237, China
| | - Wen Liu
- Environment Research Institute, Shandong University, Qingdao, 266237, China
| | - Yan Wang
- Environment Research Institute, Shandong University, Qingdao, 266237, China
| | - Xiaomin Sun
- Environment Research Institute, Shandong University, Qingdao, 266237, China.
| | - Jian Gao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
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26
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27
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Krep L, Kopp WA, Kröger LC, Döntgen M, Leonhard K. Exploring the Chemistry of Low‐Temperature Ignition by Pressure‐Accelerated Dynamics. CHEMSYSTEMSCHEM 2020. [DOI: 10.1002/syst.201900043] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Lukas Krep
- Institute of Technical Thermodynamics RWTH Aachen University Aachen 52062 Germany
| | | | | | - Malte Döntgen
- Institute of Technical Thermodynamics RWTH Aachen University Aachen 52062 Germany
- School of Engineering Brown University Providence RI 02912 USA
| | - Kai Leonhard
- Institute of Technical Thermodynamics RWTH Aachen University Aachen 52062 Germany
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28
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Jara‐Toro RA, Pino GA, Glowacki DR, Shannon RJ, Martínez‐Núñez E. Enhancing Automated Reaction Discovery with Boxed Molecular Dynamics in Energy Space. CHEMSYSTEMSCHEM 2019. [DOI: 10.1002/syst.201900024] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Rafael A. Jara‐Toro
- INIFIQC (CONICET-UNC) Dpto. De Fisicoquímica-Facultad de Ciencias Químicas-Centro Láser de Ciencias MolecularesUniversidad de Córdoba Ciudad Universitaria X50000HUA Córdoba Argentina
| | - Gustavo A. Pino
- INIFIQC (CONICET-UNC) Dpto. De Fisicoquímica-Facultad de Ciencias Químicas-Centro Láser de Ciencias MolecularesUniversidad de Córdoba Ciudad Universitaria X50000HUA Córdoba Argentina
| | - David R. Glowacki
- Centre for Computational Chemistry School of ChemistryUniversity of Bristol Cantock's Close Bristol BS8 1TS UK
| | - Robin J. Shannon
- Centre for Computational Chemistry School of ChemistryUniversity of Bristol Cantock's Close Bristol BS8 1TS UK
| | - Emilio Martínez‐Núñez
- Departmento de Química Física, Facultade de QuímicaUniversidade de Santiago de Compostela 15782 Santiago de Compostela Spain
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29
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Hermes ED, Sargsyan K, Najm HN, Zádor J. Accelerated Saddle Point Refinement through Full Exploitation of Partial Hessian Diagonalization. J Chem Theory Comput 2019; 15:6536-6549. [DOI: 10.1021/acs.jctc.9b00869] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Eric D. Hermes
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
| | - Khachik Sargsyan
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
| | - Habib N. Najm
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
| | - Judit Zádor
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
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30
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Blondal K, Jelic J, Mazeau E, Studt F, West RH, Goldsmith CF. Computer-Generated Kinetics for Coupled Heterogeneous/Homogeneous Systems: A Case Study in Catalytic Combustion of Methane on Platinum. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b01464] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Katrin Blondal
- Chemical Engineering Group, School of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - Jelena Jelic
- Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
| | - Emily Mazeau
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Felix Studt
- Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
| | - Richard H. West
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - C. Franklin Goldsmith
- Chemical Engineering Group, School of Engineering, Brown University, Providence, Rhode Island 02912, United States
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31
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32
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Cavallotti C, Pelucchi M, Georgievskii Y, Klippenstein SJ. EStokTP: Electronic Structure to Temperature- and Pressure-Dependent Rate Constants—A Code for Automatically Predicting the Thermal Kinetics of Reactions. J Chem Theory Comput 2018; 15:1122-1145. [DOI: 10.1021/acs.jctc.8b00701] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- C. Cavallotti
- Department of Chemistry, Materials and Chemical Engineering “G. Natta”, Politecnico di Milano, Milan, Italy
| | - M. Pelucchi
- Department of Chemistry, Materials and Chemical Engineering “G. Natta”, Politecnico di Milano, Milan, Italy
| | - Y. Georgievskii
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Argonne, Illinois 60439, United States
| | - S. J. Klippenstein
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Argonne, Illinois 60439, United States
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33
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A Trajectory-Based Method to Explore Reaction Mechanisms. Molecules 2018; 23:molecules23123156. [PMID: 30513663 PMCID: PMC6321347 DOI: 10.3390/molecules23123156] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 11/23/2018] [Accepted: 11/29/2018] [Indexed: 12/02/2022] Open
Abstract
The tsscds method, recently developed in our group, discovers chemical reaction mechanisms with minimal human intervention. It employs accelerated molecular dynamics, spectral graph theory, statistical rate theory and stochastic simulations to uncover chemical reaction paths and to solve the kinetics at the experimental conditions. In the present review, its application to solve mechanistic/kinetics problems in different research areas will be presented. Examples will be given of reactions involved in photodissociation dynamics, mass spectrometry, combustion chemistry and organometallic catalysis. Some planned improvements will also be described.
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34
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Rodríguez A, Rodríguez‐Fernández R, A. Vázquez S, L. Barnes G, J. P. Stewart J, Martínez‐Núñez E. tsscds2018: A code for automated discovery of chemical reaction mechanisms and solving the kinetics. J Comput Chem 2018; 39:1922-1930. [DOI: 10.1002/jcc.25370] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 05/03/2018] [Accepted: 05/11/2018] [Indexed: 01/13/2023]
Affiliation(s)
| | - Roberto Rodríguez‐Fernández
- Departamento de Química Física, Facultade de QuímicaCampus Vida, Universidade de Santiago de Compostela Santiago de Compostela 15782 Spain
| | - Saulo A. Vázquez
- Departamento de Química Física, Facultade de QuímicaCampus Vida, Universidade de Santiago de Compostela Santiago de Compostela 15782 Spain
| | - George L. Barnes
- Department of Chemistry and BiochemistrySiena College 515 Loudon Road, Loudonville New York
| | - James J. P. Stewart
- Stewart Computational Chemistry 15210 Paddington Circle, Colorado Springs Colorado 80921
| | - Emilio Martínez‐Núñez
- Departamento de Química Física, Facultade de QuímicaCampus Vida, Universidade de Santiago de Compostela Santiago de Compostela 15782 Spain
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35
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Guan Y, Ingman VM, Rooks BJ, Wheeler SE. AARON: An Automated Reaction Optimizer for New Catalysts. J Chem Theory Comput 2018; 14:5249-5261. [DOI: 10.1021/acs.jctc.8b00578] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Yanfei Guan
- Department of Chemistry, Texas A&M University, College Station, Texas 77842, United States
| | - Victoria M. Ingman
- Center for Computational Quantum Chemistry, Department of Chemistry, University of Georgia, Athens, Georgia 30602, United States
| | - Benjamin J. Rooks
- Department of Chemistry, Texas A&M University, College Station, Texas 77842, United States
| | - Steven E. Wheeler
- Department of Chemistry, Texas A&M University, College Station, Texas 77842, United States
- Center for Computational Quantum Chemistry, Department of Chemistry, University of Georgia, Athens, Georgia 30602, United States
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36
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Dewyer AL, Argüelles AJ, Zimmerman PM. Methods for exploring reaction space in molecular systems. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2017. [DOI: 10.1002/wcms.1354] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Amanda L. Dewyer
- Department of Chemistry; University of Michigan; Ann Arbor MI USA
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37
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Jacobson LD, Bochevarov AD, Watson MA, Hughes TF, Rinaldo D, Ehrlich S, Steinbrecher TB, Vaitheeswaran S, Philipp DM, Halls MD, Friesner RA. Automated Transition State Search and Its Application to Diverse Types of Organic Reactions. J Chem Theory Comput 2017; 13:5780-5797. [PMID: 28957627 DOI: 10.1021/acs.jctc.7b00764] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Transition state search is at the center of multiple types of computational chemical predictions related to mechanistic investigations, reactivity and regioselectivity predictions, and catalyst design. The process of finding transition states in practice is, however, a laborious multistep operation that requires significant user involvement. Here, we report a highly automated workflow designed to locate transition states for a given elementary reaction with minimal setup overhead. The only essential inputs required from the user are the structures of the separated reactants and products. The seamless workflow combining computational technologies from the fields of cheminformatics, molecular mechanics, and quantum chemistry automatically finds the most probable correspondence between the atoms in the reactants and the products, generates a transition state guess, launches a transition state search through a combined approach involving the relaxing string method and the quadratic synchronous transit, and finally validates the transition state via the analysis of the reactive chemical bonds and imaginary vibrational frequencies as well as by the intrinsic reaction coordinate method. Our approach does not target any specific reaction type, nor does it depend on training data; instead, it is meant to be of general applicability for a wide variety of reaction types. The workflow is highly flexible, permitting modifications such as a choice of accuracy, level of theory, basis set, or solvation treatment. Successfully located transition states can be used for setting up transition state guesses in related reactions, saving computational time and increasing the probability of success. The utility and performance of the method are demonstrated in applications to transition state searches in reactions typical for organic chemistry, medicinal chemistry, and homogeneous catalysis research. In particular, applications of our code to Michael additions, hydrogen abstractions, Diels-Alder cycloadditions, carbene insertions, and an enzyme reaction model involving a molybdenum complex are shown and discussed.
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Affiliation(s)
- Leif D Jacobson
- Schrödinger, Inc. , 120 West 45th St., New York, New York 10036, United States
| | - Art D Bochevarov
- Schrödinger, Inc. , 120 West 45th St., New York, New York 10036, United States
| | - Mark A Watson
- Schrödinger, Inc. , 120 West 45th St., New York, New York 10036, United States
| | - Thomas F Hughes
- Schrödinger, Inc. , 120 West 45th St., New York, New York 10036, United States
| | - David Rinaldo
- Schrödinger GmbH , Dynamostrasse 13, D-68165 Mannheim, Germany
| | - Stephan Ehrlich
- Schrödinger GmbH , Dynamostrasse 13, D-68165 Mannheim, Germany
| | | | - S Vaitheeswaran
- Schrödinger, Inc. , 222 Third St., Suite 2230, Cambridge, Massachusetts 02142, United States
| | - Dean M Philipp
- Schrödinger, Inc. , 101 SW Main St., Suite 1300, Portland, Oregon 97204, United States
| | - Mathew D Halls
- Schrödinger, Inc. , 5820 Oberlin Dr., Suite 203, San Diego, California 92121, United States
| | - Richard A Friesner
- Department of Chemistry, Columbia University , 3000 Broadway, New York, New York 10027, United States
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