1
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Laplaza R, Wodrich MD, Corminboeuf C. Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States. J Phys Chem Lett 2024; 15:7363-7370. [PMID: 38990895 DOI: 10.1021/acs.jpclett.4c01657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
The prediction of reaction selectivity is a challenging task for computational chemistry, not only because many molecules adopt multiple conformations but also due to the exponential relationship between effective activation energies and rate constants. To account for molecular flexibility, an increasing number of methods exist that generate conformational ensembles of transition state (TS) structures. Typically, these TS ensembles are Boltzmann weighted and used to compute selectivity assuming Curtin-Hammett conditions. This strategy, however, can lead to erroneous predictions if the appropriate filtering of the conformer ensembles is not conducted. Here, we demonstrate how any possible selectivity can be obtained by processing the same sets of TS ensembles for a model reaction. To address the burdensome filtering task in a consistent and automated way, we introduce marc, a tool for the modular analysis of representative conformers that aids in avoiding human errors while minimizing the number of reoptimization computations needed to obtain correct reaction selectivity.
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Affiliation(s)
- Ruben Laplaza
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Matthew D Wodrich
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), 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 (EPFL), 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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Joung JF, Fong MH, Roh J, Tu Z, Bradshaw J, Coley CW. Reproducing Reaction Mechanisms with Machine-Learning Models Trained on a Large-Scale Mechanistic Dataset. Angew Chem Int Ed Engl 2024:e202411296. [PMID: 38995205 DOI: 10.1002/anie.202411296] [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: 06/15/2024] [Revised: 07/11/2024] [Accepted: 07/12/2024] [Indexed: 07/13/2024]
Abstract
Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation.
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Affiliation(s)
- Joonyoung F Joung
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States
| | - Mun Hong Fong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States
| | - Jihye Roh
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States
| | - Zhengkai Tu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States
| | - John Bradshaw
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States
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3
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Fahim AM, Dacrory S, Hashem AH, Kamel S. Antimicrobial, anticancer activities, molecular docking, and DFT/B3LYP/LANL2DZ analysis of heterocyclic cellulose derivative and their Cu-complexes. Int J Biol Macromol 2024; 269:132027. [PMID: 38702001 DOI: 10.1016/j.ijbiomac.2024.132027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 04/05/2024] [Accepted: 04/29/2024] [Indexed: 05/06/2024]
Abstract
In this study, novel Cu-complexes of heterocyclic cellulose which were synthesized via the reaction of carboxymethyl cellulose (CMC) from bagasse pulp with NH2NH2 to give hydrazide cellulose which easily reacted with CS2 to form salt and then cyclized in the presence of HCl to afford cellulose oxadiazole, or with hydrazine hydrate to give cellulose triazole. Furthermore, the cellulose oxadiazole and triazole moieties acting as chelating agents with metal ion Cu (II), and all synthesized compounds were examined for their spectral analysis to show the adsorption of Cu (II) on the surface of cellulose through intramolecular hydrogen bonding. Results illustrated that cellulose oxadiazole and Cu- cellulose oxadiazole exhibited antimicrobial activities more than triazole and Cu- cellulose triazole. Furthermore, anticancer results showed that both cellulose oxadiazole and triazole exhibited activity higher than Cu-cellulose oxadiazole and Cu-cellulose triazole, where the cellulose triazole showed the highest activity (IC50 = 58.7 μg/μL). Additionally, the docking simulation of the synthesized cellulose complexes with different proteins such as PDBID:3t88, PDBID:4ynt, PDBID:1tgh, PDBID:2wje, and PDBID:4hdq and shortage bond length to confirm the experimental results. Optimization of metal complexes utilized the DFT/B3LYP/LANL2DZ basis set to confirm the stability of these metals theoretically and their physical descriptors and FMO analysis.
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Affiliation(s)
- Asmaa M Fahim
- Department of Green Chemistry, National Research Centre, Dokki, P.O. Box.12622, Cairo, Egypt
| | - Sawsan Dacrory
- Cellulose and paper Department, National Research Centre, Giza 12622, Egypt
| | - Amr H Hashem
- Botany and Microbiology Department, Faculty of Science, Al-Azhar University, Cairo 11884, Egypt.
| | - Samir Kamel
- Cellulose and paper Department, National Research Centre, Giza 12622, Egypt
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4
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Harabuchi Y, Yokoyama T, Matsuoka W, Oki T, Iwata S, Maeda S. Differentiating the Yield of Chemical Reactions Using Parameters in First-Order Kinetic Equations to Identify Elementary Steps That Control the Reactivity from Complicated Reaction Path Networks. J Phys Chem A 2024; 128:2883-2890. [PMID: 38564273 DOI: 10.1021/acs.jpca.4c00204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The yield of a chemical reaction is obtained by solving its rate equation. This study introduces an approach for differentiating yields by utilizing the parameters of the rate equation, which is expressed as a first-order linear differential equation. The yield derivative for a specific pair of reactants and products is derived by mathematically expressing the rate constant matrix contraction method, which is a simple kinetic analysis method. The parameters of the rate equation are the Gibbs energies of the intermediates and transition states in the reaction path network used to formulate the rate equation. Thus, our approach for differentiating the yield allows a numerical evaluation of the contribution of energy variation to the yield for each intermediate and transition state in the reaction path network. In other words, a comparison of these values automatically extracts the factors affecting the yield from a complicated reaction path network consisting of numerous reaction paths and intermediates. This study verifies the behavior of the proposed approach through numerical experiments on the reaction path networks of a model system and the Rh-catalyzed hydroformylation reaction. Moreover, the possibility of using this approach for designing ligands in organometallic catalysts is discussed.
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Affiliation(s)
- Yu Harabuchi
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
- JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Tomohiko Yokoyama
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Wataru Matsuoka
- JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Taihei Oki
- JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Satoru Iwata
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
- JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Satoshi Maeda
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
- JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
- Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
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5
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Vadaddi SM, Zhao Q, Savoie BM. Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability. J Phys Chem A 2024; 128:2543-2555. [PMID: 38517281 DOI: 10.1021/acs.jpca.3c07240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.
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Affiliation(s)
- Sai Mahit Vadaddi
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Qiyuan Zhao
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Brett M Savoie
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
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6
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Nicolle A, Deng S, Ihme M, Kuzhagaliyeva N, Ibrahim EA, Farooq A. Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview. J Chem Inf Model 2024; 64:597-620. [PMID: 38284618 DOI: 10.1021/acs.jcim.3c01633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.
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Affiliation(s)
- Andre Nicolle
- Aramco Fuel Research Center, Rueil-Malmaison 92852, France
| | - Sili Deng
- Massachusetts Institute of Technology, Cambridge 02139, Massachusetts, United States
| | - Matthias Ihme
- Stanford University, Stanford 94305, California, United States
| | | | - Emad Al Ibrahim
- King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | - Aamir Farooq
- King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
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7
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Zhang Y, Xu C, Lan Z. Automated Exploration of Reaction Networks and Mechanisms Based on Metadynamics Nanoreactor Simulations. J Chem Theory Comput 2023. [PMID: 38031422 DOI: 10.1021/acs.jctc.3c00752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
We developed an automated approach to construct a complex reaction network and explore the reaction mechanisms for numerous reactant molecules by integrating several theoretical approaches. Nanoreactor-type molecular dynamics was used to generate possible chemical reactions, in which the metadynamics was used to overcome the reaction barriers, and the semiempirical GFN2-xTB method was used to reduce the computational cost. Reaction events were identified from trajectories using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for further transition-state searches at the electronic structure levels of density functional theory to obtain the reaction mechanism. Finally, the entire reaction network containing multiple pathways was built. The feasibility and efficiency of the automated construction of the reaction network were investigated using the HCHO and NH3 biomolecular reaction and the reaction network for a multispecies system comprising dozens of HCN and H2O molecules. The results indicated that the proposed approach provides a valuable and effective tool for the automated exploration of the reaction networks.
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Affiliation(s)
- Yutai Zhang
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
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8
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Zhao Q, Anstine DM, Isayev O, Savoie BM. Δ 2 machine learning for reaction property prediction. Chem Sci 2023; 14:13392-13401. [PMID: 38033903 PMCID: PMC10686042 DOI: 10.1039/d3sc02408c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 07/11/2023] [Indexed: 12/02/2023] Open
Abstract
The emergence of Δ-learning models, whereby machine learning (ML) is used to predict a correction to a low-level energy calculation, provides a versatile route to accelerate high-level energy evaluations at a given geometry. However, Δ-learning models are inapplicable to reaction properties like heats of reaction and activation energies that require both a high-level geometry and energy evaluation. Here, a Δ2-learning model is introduced that can predict high-level activation energies based on low-level critical-point geometries. The Δ2 model uses an atom-wise featurization typical of contemporary ML interatomic potentials (MLIPs) and is trained on a dataset of ∼167 000 reactions, using the GFN2-xTB energy and critical-point geometry as a low-level input and the B3LYP-D3/TZVP energy calculated at the B3LYP-D3/TZVP critical point as a high-level target. The excellent performance of the Δ2 model on unseen reactions demonstrates the surprising ease with which the model implicitly learns the geometric deviations between the low-level and high-level geometries that condition the activation energy prediction. The transferability of the Δ2 model is validated on several external testing sets where it shows near chemical accuracy, illustrating the benefits of combining ML models with readily available physical-based information from semi-empirical quantum chemistry calculations. Fine-tuning of the Δ2 model on a small number of Gaussian-4 calculations produced a 35% accuracy improvement over DFT activation energy predictions while retaining xTB-level cost. The Δ2 model approach proves to be an efficient strategy for accelerating chemical reaction characterization with minimal sacrifice in prediction accuracy.
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Affiliation(s)
- Qiyuan Zhao
- Davidson School of Chemical Engineering, Purdue University West Lafayette IN 47906 USA
| | - Dylan M Anstine
- Department of Chemistry, Carnegie Mellon University Pittsburgh PA 15213 USA
| | - Olexandr Isayev
- Department of Chemistry, Carnegie Mellon University Pittsburgh PA 15213 USA
| | - Brett M Savoie
- Davidson School of Chemical Engineering, Purdue University West Lafayette IN 47906 USA
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9
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Zhao Q, Garimella SS, Savoie BM. Thermally Accessible Prebiotic Pathways for Forming Ribonucleic Acid and Protein Precursors from Aqueous Hydrogen Cyanide. J Am Chem Soc 2023; 145:6135-6143. [PMID: 36883252 DOI: 10.1021/jacs.2c11857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
The search for prebiotic chemical pathways to biologically relevant molecules is a long-standing puzzle that has generated a menagerie of competing hypotheses with limited experimental prospects for falsification. However, the advent of computational network exploration methodologies has created the opportunity to compare the kinetic plausibility of various channels and even propose new pathways. Here, the space of organic molecules that can be formed within four polar or pericyclic reactions from water and hydrogen cyanide (HCN), two established prebiotic candidates for generating biological precursors, was comprehensively explored with a state-of-the-art exploration algorithm. A surprisingly diverse reactivity landscape was revealed within just a few steps of these simple molecules. Reaction pathways to several biologically relevant molecules were discovered involving lower activation energies and fewer reaction steps compared with recently proposed alternatives. Accounting for water-catalyzed reactions qualitatively affects the interpretation of the network kinetics. The case-study also highlights omissions of simpler and lower barrier reaction pathways to certain products by other algorithms that qualitatively affect the interpretation of HCN reactivity.
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Affiliation(s)
- Qiyuan Zhao
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Sanjay S Garimella
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Brett M Savoie
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
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10
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Bayrak H, Fahim AM, Yaylaci Karahalil F, Azafad I, Boyraci GM, Taflan E. Synthesis, antioxidant activity, docking simulation, and computational investigation of novel heterocyclic compounds and Schiff Bases from picric acid. J Mol Struct 2023. [DOI: 10.1016/j.molstruc.2023.135184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
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11
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Synthesis, biological activities, molecular docking, theoretical calculations of some 1,3,4-oxadiazoles, 1,2,4-triazoles, and 1,2,4-triazolo[3,4-b]-1,3,4-thiadiazines derivatives. J Mol Struct 2023. [DOI: 10.1016/j.molstruc.2023.135238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
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12
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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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13
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Nakao A, Harabuchi Y, Maeda S, Tsuda K. Exploring the Quantum Chemical Energy Landscape with GNN-Guided Artificial Force. J Chem Theory Comput 2023; 19:713-717. [PMID: 36689311 PMCID: PMC9933424 DOI: 10.1021/acs.jctc.2c01061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Artificial force has been proven useful to get over energy barriers and quickly search a large portion of the energy landscape. This work proposes a method based on graph neural networks to optimize the choice of transformation patterns to examine and accelerate energy landscape exploration. In open search from glutathione, the search efficiency was largely improved in comparison to random selection. We also applied transfer learning from glutathione to tuftsin, resulting in further efficiency gains.
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Affiliation(s)
- Atsuyuki Nakao
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa277-8561, Japan
| | - Yu Harabuchi
- Institute
for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo001-0021, Japan,JST
ERATO Maeda Artificial Intelligence for Chemical Reaction Design and
Discovery Project, Sapporo060-0810, Japan,Department
of Chemistry, Faculty of Science, Hokkaido
University, Sapporo060-0810, Japan
| | - Satoshi Maeda
- Institute
for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo001-0021, Japan,JST
ERATO Maeda Artificial Intelligence for Chemical Reaction Design and
Discovery Project, Sapporo060-0810, Japan,Department
of Chemistry, Faculty of Science, Hokkaido
University, Sapporo060-0810, Japan
| | - Koji Tsuda
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa277-8561, Japan,RIKEN
Center for Advanced Intelligence Project, Tokyo103-0027, Japan,Research
and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba305-0047, Japan,
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14
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Mita T, Takano H, Hayashi H, Kanna W, Harabuchi Y, Houk KN, Maeda S. Prediction of High-Yielding Single-Step or Cascade Pericyclic Reactions for the Synthesis of Complex Synthetic Targets. J Am Chem Soc 2022; 144:22985-23000. [PMID: 36451276 DOI: 10.1021/jacs.2c09830] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Pericyclic reactions, which involve cyclic concerted transition states without ionic or radical intermediates, have been extensively studied since their definition in the 1960s, and the famous Woodward-Hoffmann rules predict their stereoselectivity and chemoselectivity. Here, we describe the application of a fully automated reaction-path search method, that is, the artificial force induced reaction (AFIR), to trace an input compound back to reasonable starting materials through thermally allowed pericyclic reactions via product-based quantum-chemistry-aided retrosynthetic analysis (QCaRA) without using any a priori experimental knowledge. All categories of pericyclic reactions, including cycloadditions, ene reactions, group-transfer, cheletropic, electrocyclic, and sigmatropic reactions, were successfully traced back via concerted reaction pathways, and starting materials were computationally obtained with the correct stereochemistry. Furthermore, AFIR was used to predict whether the identified reaction pathway can be expected to occur in good yield relative to other possible reactions of the identified starting material. In order to showcase its practical utility, this state-of-the-art technology was also applied to the retrosynthetic analysis of a natural product with a relatively high number of atoms (52 atoms: endiandric acid C methyl ester), which was first synthesized by Nicolaou in 1982 and provided the corresponding starting polyenes with the correct stereospecificity via three pericyclic reaction cascades (one Diels-Alder reaction as well as 6π and 8π electrocyclic reactions). Moreover, not only systems that obey the Woodward-Hoffmann rules but also systems that violate these rules, such as those recently calculated by Houk, can be retrosynthesized accurately.
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Affiliation(s)
- Tsuyoshi Mita
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.,JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Hideaki Takano
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.,JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Hiroki Hayashi
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.,JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Wataru Kanna
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Yu Harabuchi
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.,JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan.,Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - K N Houk
- Department of Chemical and Biomolecular Engineering and Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, United States
| | - Satoshi Maeda
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.,JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan.,Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan.,Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0044, Japan
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15
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Takahashi L, Yoshida S, Fujima J, Oikawa H, Takahashi K. Unveiling the reaction pathways of hydrocarbons via experiments, computations and data science. Phys Chem Chem Phys 2022; 24:29841-29849. [PMID: 36468419 DOI: 10.1039/d2cp04499d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Reaction networks of hydrocarbons are explored using first principles calculations, data science, and experiments. Transforming hydrocarbon data into networks reveals the prevalence of the formation and reaction of various molecules. Graph theory is implemented to extract knowledge from the reaction network. In particular, centralities analysis reveals that H+, CCC, CH3+, CC, and [CH2+]C have high degrees and are thus very likely to form or react with other molecules. Additionally, H+, CH3+, C2H5+, C8H15+, C8H17+, and C6H11+ are found to have high control throughout the network and lead towards a series of additional reactions. The constructed network is also validated in experiments while the shortest path analysis is implemented for further comparison between experiment and the network. Thus, combining network analysis with first principles calculations uncovers key points in the development of various hydrocarbons that can be used to improve catalyst design and targeted synthesis of desired hydrocarbons.
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Affiliation(s)
- Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
| | - Shigehiro Yoshida
- Innovative Research Excellence, Power unit & Energy, Honda R&D Co., Ltd., 3-15-1 Senzui, Asaka, Saitama, 351-0024, Japan.
| | - Jun Fujima
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
| | - Hiroshi Oikawa
- Innovative Research Excellence, Power unit & Energy, Honda R&D Co., Ltd., 3-15-1 Senzui, Asaka, Saitama, 351-0024, Japan.
| | - Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
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16
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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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17
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Fahim AM. Anti-proliferative activity, Molecular docking study of Novel synthesized Ethoxyphenylbenzenesulfonamide with Computational Calculations. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.134871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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18
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Lavigne C, Gomes G, Pollice R, Aspuru-Guzik A. Guided discovery of chemical reaction pathways with imposed activation. Chem Sci 2022; 13:13857-13871. [PMID: 36544742 PMCID: PMC9710306 DOI: 10.1039/d2sc05135d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 11/09/2022] [Indexed: 11/12/2022] Open
Abstract
Computational power and quantum chemical methods have improved immensely since computers were first applied to the study of reactivity, but the de novo prediction of chemical reactions has remained challenging. We show that complex reaction pathways can be efficiently predicted in a guided manner using chemical activation imposed by geometrical constraints of specific reactive modes, which we term imposed activation (IACTA). Our approach is demonstrated on realistic and challenging chemistry, such as a triple cyclization cascade involved in the total synthesis of a natural product, a water-mediated Michael addition, and several oxidative addition reactions of complex drug-like molecules. Notably and in contrast with traditional hand-guided computational chemistry calculations, our method requires minimal human involvement and no prior knowledge of the products or the associated mechanisms. We believe that IACTA will be a transformational tool to screen for chemical reactivity and to study both by-product formation and decomposition pathways in a guided way.
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Affiliation(s)
- Cyrille Lavigne
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada
| | - Gabe Gomes
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto80 St George StTorontoOntarioM5S 3H6Canada
| | - Robert Pollice
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto80 St George StTorontoOntarioM5S 3H6Canada
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto80 St George StTorontoOntarioM5S 3H6Canada,Department of Chemical Engineering & Applied Chemistry, University of Toronto200 College St.OntarioM5S 3E5Canada,Department of Materials Science & Engineering, University of Toronto184 College St.OntarioM5S 3E4Canada,Vector Institute for Artificial Intelligence661 University Ave Suite 710TorontoOntarioM5G 1M1Canada,Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR)661 University AveTorontoOntarioM5GCanada
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19
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Zhao Q, Savoie BM. Algorithmic Explorations of Unimolecular and Bimolecular Reaction Spaces. Angew Chem Int Ed Engl 2022; 61:e202210693. [PMID: 36074520 PMCID: PMC9827825 DOI: 10.1002/anie.202210693] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Indexed: 01/12/2023]
Abstract
Algorithmic reaction exploration based on transition state searches has already made inroads into many niche applications, but its potential as a general-purpose tool is still largely unrealized. Computational cost and the absence of benchmark problems involving larger molecules remain obstacles to further progress. Here an ultra-low cost exploration algorithm is implemented and used to explore the reactivity of unimolecular and bimolecular reactants, comprising a total of 581 reactions involving 51 distinct reactants. The algorithm discovers all established reaction pathways, where such comparisons are possible, while also revealing a much richer reactivity landscape, including lower barrier reaction pathways and a strong dependence of reaction conformation in the apparent barriers of the reported reactions. The diversity of these benchmarks illustrate that reaction exploration algorithms are approaching general-purpose capability.
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Affiliation(s)
- Qiyuan Zhao
- Davidson School of Chemical EngineeringPurdue UniversityWest LafayetteIN47906USA
| | - Brett M. Savoie
- Davidson School of Chemical EngineeringPurdue UniversityWest LafayetteIN47906USA
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20
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Ramos-Sánchez P, Harvey JN, Gámez JA. An automated method for graph-based chemical space exploration and transition state finding. J Comput Chem 2022; 44:27-42. [PMID: 36239971 DOI: 10.1002/jcc.27011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/28/2022] [Accepted: 09/05/2022] [Indexed: 12/24/2022]
Abstract
Algorithms that automatically explore the chemical space have been limited to chemical systems with a low number of atoms due to expensive involved quantum calculations and the large amount of possible reaction pathways. The method described here presents a novel solution to the problem of chemical exploration by generating reaction networks with heuristics based on chemical theory. First, a second version of the reaction network is determined through molecular graph transformations acting upon functional groups of the reacting. Only transformations that break two chemical bonds and form two new ones are considered, leading to a significant performance enhancement compared to previously presented algorithm. Second, energy barriers for this reaction network are estimated through quantum chemical calculations by a growing string method, which can also identify non-octet species missed during the previous step and further define the reaction network. The proposed algorithm has been successfully applied to five different chemical reactions, in all cases identifying the most important reaction pathways.
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Affiliation(s)
- Pablo Ramos-Sánchez
- Digital R&D, Covestro Deutschland AG, Leverkusen, Germany.,Department of Chemistry, KU Leuven, Leuven, Belgium
| | | | - José A Gámez
- Digital R&D, Covestro Deutschland AG, Leverkusen, Germany
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21
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Mechanistic elucidation of Diels–Alder cycloaddition reactions between quinoflavonoid and substituted butadiene using LOL, ELF, QTAIM, and DFT studies. Struct Chem 2022. [DOI: 10.1007/s11224-022-02058-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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22
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Unsleber JP, Grimmel SA, Reiher M. Chemoton 2.0: Autonomous Exploration of Chemical Reaction Networks. J Chem Theory Comput 2022; 18:5393-5409. [PMID: 35926118 DOI: 10.1021/acs.jctc.2c00193] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Fueled by advances in hardware and algorithm design, large-scale automated explorations of chemical reaction space have become possible. Here, we present our approach to an open-source, extensible framework for explorations of chemical reaction mechanisms based on the first-principles of quantum mechanics. It is intended to facilitate reaction network explorations for diverse chemical problems with a wide range of goals such as mechanism elucidation, reaction path optimization, retrosynthetic path validation, reagent design, and microkinetic modeling. The stringent first-principles basis of all algorithms in our framework is key for the general applicability that avoids any restrictions to specific chemical systems. Such an agile framework requires multiple specialized software components of which we present three modules in this work. The key module, Chemoton, drives the exploration of reaction networks. For the exploration itself, we introduce two new algorithms for elementary-step searches that are based on Newton trajectories. The performance of these algorithms is assessed for a variety of reactions characterized by a broad chemical diversity in terms of bonding patterns and chemical elements. Chemoton successfully recovers the vast majority of these. We provide the resulting data, including large numbers of reactions that were not included in our reference set, to be used as a starting point for further explorations and for future reference.
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Affiliation(s)
- Jan P Unsleber
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Stephanie A Grimmel
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Markus Reiher
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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23
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Spiekermann KA, Pattanaik L, Green WH. Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy. J Phys Chem A 2022; 126:3976-3986. [PMID: 35727075 DOI: 10.1021/acs.jpca.2c02614] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Quantitative estimates of reaction barriers are essential for developing kinetic mechanisms and predicting reaction outcomes. However, the lack of experimental data and the steep scaling of accurate quantum calculations often hinder the ability to obtain reliable kinetic values. Here, we train a directed message passing neural network on nearly 24,000 diverse gas-phase reactions calculated at CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP. Our model uses 75% fewer parameters than previous studies, an improved reaction representation, and proper data splits to accurately estimate performance on unseen reactions. Using information from only the reactant and product, our model quickly predicts barrier heights with a testing MAE of 2.6 kcal mol-1 relative to the coupled-cluster data, making it more accurate than a good density functional theory calculation. Furthermore, our results show that future modeling efforts to estimate reaction properties would significantly benefit from fine-tuning calibration using a transfer learning technique. We anticipate this model will accelerate and improve kinetic predictions for small molecule chemistry.
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Affiliation(s)
- Kevin A Spiekermann
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Lagnajit Pattanaik
- 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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24
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Liu Y, Qi H, Lei M. Elastic Image Pair Method for Finding Transition States on Potential Energy Surfaces Using Only First Derivatives. J Chem Theory Comput 2022; 18:5108-5115. [PMID: 35771528 DOI: 10.1021/acs.jctc.2c00137] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Herein, an elastic image pair (EIP) method is proposed to search transition states between two potential-energy minima using only first derivatives. In this method, two images are generated, and the spring forces are added to the images to control the distance between the two images. Transition states are reached when the force and the distance of the image pair are both converged. A set of test molecules is optimized using the EIP method, which shows its efficiency in transition state searching compared to other methods. This new method is more stable and reliable in finding transition states with much less computations.
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Affiliation(s)
- Yangqiu Liu
- State Key Laboratory of Chemical Resource Engineering, Institute of Computational Chemistry, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China
| | - Hexiang Qi
- State Key Laboratory of Chemical Resource Engineering, Institute of Computational Chemistry, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China
| | - Ming Lei
- State Key Laboratory of Chemical Resource Engineering, Institute of Computational Chemistry, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China
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25
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Two-state reactivity in the acetylene cyclotrimerization reaction catalyzed by a single atomic transition-metal ion: The case for V+ and Fe+. COMPUT THEOR CHEM 2022. [DOI: 10.1016/j.comptc.2022.113682] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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26
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Zhao Q, Hsu HH, Savoie BM. Conformational Sampling for Transition State Searches on a Computational Budget. J Chem Theory Comput 2022; 18:3006-3016. [PMID: 35403426 DOI: 10.1021/acs.jctc.2c00081] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Qiyuan Zhao
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Hsuan-Hao Hsu
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Brett M. Savoie
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
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27
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Rasmussen MH, Jensen JH. Fast and automated identification of reactions with low barriers using meta-MD simulations. PEERJ PHYSICAL CHEMISTRY 2022. [DOI: 10.7717/peerj-pchem.22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
We test our meta-molecular dynamics (MD) based approach for finding low-barrier (<30 kcal/mol) reactions on uni- and bimolecular reactions extracted from the barrier dataset developed by Grambow, Pattanaik & Green (2020). For unimolecular reactions the meta-MD simulations identify 25 of the 26 products found by Grambow, Pattanaik & Green (2020), while the subsequent semiempirical screening eliminates an additional four reactions due to an overestimation of the reaction energies or estimated barrier heights relative to DFT. In addition, our approach identifies 36 reactions not found by Grambow, Pattanaik & Green (2020), 10 of which are <30 kcal/mol. For bimolecular reactions the meta-MD simulations identify 19 of the 20 reactions found by Grambow, Pattanaik & Green (2020), while the subsequent semiempirical screening eliminates an additional reaction. In addition, we find 34 new low-barrier reactions. For bimolecular reactions we found that it is necessary to “encourage” the reactants to go to previously undiscovered products, by including products found by other MD simulations when computing the biasing potential as well as decreasing the size of the molecular cavity in which the MD occurs, until a reaction is observed. We also show that our methodology can find the correct products for two reactions that are more representative of those encountered in synthetic organic chemistry. The meta-MD hyperparameters used in this study thus appear to be generally applicable to finding low-barrier reactions.
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28
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Matsuoka W, Harabuchi Y, Maeda S. Virtual Ligand-Assisted Screening Strategy to Discover Enabling Ligands for Transition Metal Catalysis. ACS Catal 2022. [DOI: 10.1021/acscatal.2c00267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Wataru Matsuoka
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
| | - Yu Harabuchi
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan
- ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
| | - Satoshi Maeda
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan
- ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0044, Japan
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29
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Synthesis of novel d-α-galactopyranosyl-l-seryl/l-threonyl-l-alanyl-l-alanine as useful precursors of new glycopeptide antibiotics with computational calculations studies. Carbohydr Res 2022; 514:108546. [DOI: 10.1016/j.carres.2022.108546] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 03/19/2022] [Accepted: 03/25/2022] [Indexed: 12/24/2022]
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30
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Synthesis of novel 1,2,3-triazole based acridine and benzothiazole scaffold N-glycosides with anti-proliferative activity, docking studies, and comparative computational studies. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2021.131941] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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31
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Isamura BK, Lobb KA. New Insights into the (A)Synchronicity of Diels-Alder Reactions: A Theoretical Study Based on the Reaction Force Analysis and Atomic Resolution of Energy Derivatives. Molecules 2022; 27:1546. [PMID: 35268648 PMCID: PMC8911883 DOI: 10.3390/molecules27051546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 01/31/2022] [Accepted: 02/08/2022] [Indexed: 02/04/2023] Open
Abstract
In the present manuscript, we report new insights into the concept of (a)synchronicity in Diels-Alder (DA) reactions in the framework of the reaction force analysis in conjunction with natural population calculations and the atomic resolution of energy derivatives along the intrinsic reaction coordinate (IRC) path. Our findings suggest that the DA reaction transitions from a preferentially concerted mechanism to a stepwise one in a 0.10 Å window of synchronicity indices ranging from 0.90 to 1.00 Å. We have also shown that the relative position of the global minimum of the reaction force constant with respect to the TS is an alternative and quantifiable indicator of the (a)synchronicity in DA reactions. Moreover, the atomic resolution of energy derivatives reveals that the mechanism of the DA reaction involves two inner elementary processes associated with the formation of each of the two C-C bonds. This resolution goes on to indicate that, in asynchronous reactions, the driving and retarding components of the reaction force are mostly due to the fast and slow-forming C-C bonds (elementary processes) respectively, while in synchronous reactions, both elementary processes retard and drive the process concomitantly and equivalently.
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Affiliation(s)
| | - Kevin Alan Lobb
- Department of Chemistry, Rhodes University, Makhanda 6140, South Africa;
- Research Unit in BioInformatics (RUBi), Rhodes University, Makhanda 6140, South Africa
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32
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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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33
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Chen X, Liu M, Gao J. CARNOT: a Fragment-Based Direct Molecular Dynamics and Virtual-Reality Simulation Package for Reactive Systems. J Chem Theory Comput 2022; 18:1297-1313. [PMID: 35129348 DOI: 10.1021/acs.jctc.1c01032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Traditionally, the study of reaction mechanisms of complex reaction systems such as combustion has been performed on an individual basis by optimizations of transition structure and minimum energy path or by reaction dynamics trajectory calculations for one elementary reaction at a time. It is effective, but time-consuming, whereas important and unexpected processes could have been missed. In this article, we present a direct molecular dynamics (DMD) approach and a virtual-reality simulation program, CARNOT, in which plausible chemical reactions are simulated simultaneously at finite temperature and pressure conditions. A key concept of the present ab initio molecular dynamics method is to partition a large, chemically reactive system into molecular fragments that can be adjusted on the fly of a DMD simulation. The theory represents an extension of the explicit polarization method to reactive events, called ReX-Pol. We propose a highest-and-lowest adapted-spin approximation to define the local spins of individual fragments, rather than treating the entire system by a delocalized wave function. Consequently, the present ab initio DMD can be applied to reactive systems consisting of an arbitrarily varying number of closed and open-shell fragments such as free radicals, zwitterions, and separate ions found in combustion and other reactions. A graph-data structure algorithm was incorporated in CARNOT for the analysis of reaction networks, suitable for reaction mechanism reduction. Employing the PW91 density functional theory and the 6-31+G(d) basis set, the capabilities of the CARNOT program were illustrated by a combustion reaction, consisting of 28 650 atoms, and by reaction network analysis that revealed a range of mechanistic and dynamical events. The method may be useful for applications to other types of complex reactions.
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Affiliation(s)
- Xin Chen
- Peking University Shenzhen Graduate School, Shenzhen, Guangdong 581055, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 581055, China
| | - Meiyi Liu
- Peking University Shenzhen Graduate School, Shenzhen, Guangdong 581055, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 581055, China
| | - Jiali Gao
- Peking University Shenzhen Graduate School, Shenzhen, Guangdong 581055, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 581055, China.,Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
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34
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Garay-Ruiz D, Álvarez-Moreno M, Bo C, Martínez-Núñez E. New Tools for Taming Complex Reaction Networks: The Unimolecular Decomposition of Indole Revisited. ACS PHYSICAL CHEMISTRY AU 2022; 2:225-236. [PMID: 36855573 PMCID: PMC9718323 DOI: 10.1021/acsphyschemau.1c00051] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The level of detail attained in the computational description of reaction mechanisms can be vastly improved through tools for automated chemical space exploration, particularly for systems of small to medium size. Under this approach, the unimolecular decomposition landscape for indole was explored through the automated reaction mechanism discovery program AutoMeKin. Nevertheless, the sheer complexity of the obtained mechanisms might be a hindrance regarding their chemical interpretation. In this spirit, the new Python library amk-tools has been designed to read and manipulate complex reaction networks, greatly simplifying their overall analysis. The package provides interactive dashboards featuring visualizations of the network, the three-dimensional (3D) molecular structures and vibrational normal modes of all chemical species, and the corresponding energy profiles for selected pathways. The combination of the joined mechanism generation and postprocessing workflow with the rich chemistry of indole decomposition enabled us to find new details of the reaction (obtained at the CCSD(T)/aug-cc-pVTZ//M06-2X/MG3S level of theory) that were not reported before: (i) 16 pathways leading to the formation of HCN and NH3 (via amino radical); (ii) a barrierless reaction between methylene radical and phenyl isocyanide, which might be an operative mechanism under the conditions of the interstellar medium; and (iii) reaction channels leading to both hydrogen cyanide and hydrogen isocyanide, of potential astrochemical interest as the computed HNC/HCN ratios greatly exceed the calculated equilibrium value at very low temperatures. The reported reaction networks can be very valuable to supplement databases of kinetic data, which is of remarkable interest for pyrolysis and astrochemical studies.
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Affiliation(s)
- Diego Garay-Ruiz
- Institute
of Chemical Research of Catalonia (ICIQ), Barcelona Institute of Science & Technology (BIST), Avinguda Països Catalans,
16, 43007 Tarragona, Spain,Departament
de Química Física i Inorgànica, Universitat Rovira i Virgili (URV), Marcel·lí Domingo s/n, 43007 Tarragona, Spain
| | - Moises Álvarez-Moreno
- Institute
of Chemical Research of Catalonia (ICIQ), Barcelona Institute of Science & Technology (BIST), Avinguda Països Catalans,
16, 43007 Tarragona, Spain
| | - Carles Bo
- Institute
of Chemical Research of Catalonia (ICIQ), Barcelona Institute of Science & Technology (BIST), Avinguda Països Catalans,
16, 43007 Tarragona, Spain,Departament
de Química Física i Inorgànica, Universitat Rovira i Virgili (URV), Marcel·lí Domingo s/n, 43007 Tarragona, Spain,
| | - Emilio Martínez-Núñez
- Departmento
de Química Física, Facultade de Química, Universidade de Santiago de Compostela, 15782 Santiago
de Compostela, Spain,
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35
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Cui Q, Peng J, Xu C, Lan Z. Automatic Approach to Explore the Multireaction Mechanism for Medium-Sized Bimolecular Reactions via Collision Dynamics Simulations and Transition State Searches. J Chem Theory Comput 2022; 18:910-924. [DOI: 10.1021/acs.jctc.1c00795] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Qinghai Cui
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Jiawei Peng
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
- Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education; School of Chemistry, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
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36
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Baiardi A, Grimmel SA, Steiner M, Türtscher PL, Unsleber JP, Weymuth T, Reiher M. Expansive Quantum Mechanical Exploration of Chemical Reaction Paths. Acc Chem Res 2022; 55:35-43. [PMID: 34918903 DOI: 10.1021/acs.accounts.1c00472] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Quantum mechanical methods have been well-established for the elucidation of reaction paths of chemical processes and for the explicit dynamics of molecular systems. While they are usually deployed in routine manual calculations on reactions for which some insights are already available (typically from experiment), new algorithms and continuously increasing capabilities of modern computer hardware allow for exploratory open-ended computational campaigns that are unbiased and therefore enable unexpected discoveries. Highly efficient and even automated procedures facilitate systematic approaches toward the exploration of uncharted territory in molecular transformations and dynamics. In this work, we elaborate on such explorative approaches that range from reaction network explorations with (stationary) quantum chemical methods to explorative molecular dynamics and migrant wave packet dynamics. The focus is on recent developments that cover the following strategies. (i) Pruning search options for elementary reaction steps by heuristic rules based on the first-principles of quantum mechanics: Rules are required for reducing the combinatorial explosion of potentially reactive atom pairings, and rooting them in concepts derived from the electronic wave function makes them applicable to any molecular system. (ii) Enforcing reactive events by external biases: Inducing a reaction requires constraints that steer and direct elementary-step searches, which can be formulated in terms of forces, velocities, or supplementary potentials. (iii) Manual steering facilitated by interactive quantum mechanics: As ultrafast quantum chemical methods allow for real-time manual interactions with molecular systems, human-intuition-guided paths can be easily explored with suitable human-machine interfaces. (iv) New approaches for transition-state optimization with continuous curve representations can provide stable schemes to be driven in an automated way by allowing for an efficient tuning of the curve's parameters (instead of a manipulation of a collection of structures along the path), and (v) reactive molecular dynamics and direct wave packet propagation exploit the equations of motion of an underlying mechanical theory (usually, classical Newtonian mechanics or Schrödinger quantum mechanics). Explorative approaches are likely to replace the current state of the art in computational chemistry, because they reduce the human effort to be invested in reaction path elucidations, they are less prone to errors and bias-free, and they cover more extensive regions of the relevant configuration space. As a result, computational investigations that rely on these techniques are more likely to deliver surprising discoveries.
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Affiliation(s)
- Alberto Baiardi
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Stephanie A. Grimmel
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Miguel Steiner
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Paul L. Türtscher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jan P. Unsleber
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Thomas Weymuth
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Markus Reiher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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37
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Hirai H, Jinnouchi R. Discovering surface reaction pathways using accelerated molecular dynamics and network analysis tools. RSC Adv 2022; 12:23274-23283. [PMID: 36090391 PMCID: PMC9382359 DOI: 10.1039/d2ra04343b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/10/2022] [Indexed: 11/21/2022] Open
Abstract
We present an automated method that maps surface reaction pathways with no experimental data and with minimal human interventions. In this method, bias potentials promoting surface reactions are applied to enable statistical samplings of the surface reaction within the timescale of ab initio molecular dynamics (MD) simulations, and elementary reactions are extracted automatically using an extension of the method constructed for gas- or liquid-phase reactions. By converting the extracted elementary reaction data to directed graph data, MD trajectories can be efficiently mapped onto reaction pathways using a network analysis tool. To demonstrate the power of the method, it was applied to the steam reforming of methane on the Rh(111) surface and to propane reforming on the Pt(111) and Pt3Sn(111) surfaces. We discover new energetically favorable pathways for both reactions and reproduce the experimentally-observed materials-dependence of the surface reaction activity and the selectivity for the propane reforming reactions. We present an automated method that maps surface reaction pathways with no experimental data and with minimal human interventions.![]()
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Affiliation(s)
- Hirotoshi Hirai
- Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute, Aichi 480-1192, Japan
| | - Ryosuke Jinnouchi
- Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute, Aichi 480-1192, Japan
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38
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Caramelli D, Granda J, Mehr SHM, Cambié D, Henson AB, Cronin L. Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network. ACS CENTRAL SCIENCE 2021; 7:1821-1830. [PMID: 34849401 PMCID: PMC8620554 DOI: 10.1021/acscentsci.1c00435] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Indexed: 05/04/2023]
Abstract
We present a robotic chemical discovery system capable of navigating a chemical space based on a learned general association between molecular structures and reactivity, while incorporating a neural network model that can process data from online analytics and assess reactivity without knowing the identity of the reagents. Working in conjunction with this learned knowledge, our robotic platform is able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown chemical spaces, regardless of the identity of the starting materials. Through the system, we identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, and some unpredictable reactions that yielded new molecules. The validation of the system was done within a budget of 15 inputs combined in 1018 reactions, further analysis of which allowed us to discover not only a new photochemical reaction but also a new reactivity mode for a well-known reagent (p-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of 6 equiv of TosMIC in a "multistep, single-substrate" cascade reaction yielding a trimeric product in high yield (47% unoptimized) with the formation of five new C-C bonds involving sp-sp2 and sp-sp3 carbon centers. An analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of a reactivity-first robotic discovery of unknown reaction methodologies without requiring human input.
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39
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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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40
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Kim J, Gu GH, Noh J, Kim S, Gim S, Choi J, Jung Y. Predicting potentially hazardous chemical reactions using an explainable neural network. Chem Sci 2021; 12:11028-11037. [PMID: 34522300 PMCID: PMC8386654 DOI: 10.1039/d1sc01049b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 07/12/2021] [Indexed: 11/21/2022] Open
Abstract
Predicting potentially dangerous chemical reactions is a critical task for laboratory safety. However, a traditional experimental investigation of reaction conditions for possible hazardous or explosive byproducts entails substantial time and cost, for which machine learning prediction could accelerate the process and help detailed experimental investigations. Several machine learning models have been developed which allow the prediction of major chemical reaction products with reasonable accuracy. However, these methods may not present sufficiently high accuracy for the prediction of hazardous products which particularly requires a low false negative result for laboratory safety in order not to miss any dangerous reactions. In this work, we propose an explainable artificial intelligence model that can predict the formation of hazardous reaction products in a binary classification fashion. The reactant molecules are transformed into substructure-encoded fingerprints and then fed into a convolutional neural network to make the binary decision of the chemical reaction. The proposed model shows a false negative rate of 0.09, which can be compared with 0.47-0.66 using the existing main product prediction models. To provide explanations for what substructures of the given reactant molecules are important to make a decision for target hazardous product formation, we apply an input attribution method, layer-wise relevance propagation, which computes the contributions of individual inputs per input data. The computed attributions indeed match some of the existing chemical intuitions and mechanisms, and also offer a way to analyze possible data-imbalance issues of the current predictions based on relatively small positive datasets. We expect that the proposed hazardous product prediction model will be complementary to existing main product prediction models and experimental investigations.
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Affiliation(s)
- Juhwan Kim
- Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea
| | - Geun Ho Gu
- Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea
| | - Juhwan Noh
- Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea
| | - Seongun Kim
- Graduate School of Artificial Intelligence KAIST Daejeon: 291 Daehak-ro, N24, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Suji Gim
- Environment & Safety Research Center, Samsung Electronics Co. 1, Samsungjeonja-ro Hwasung-si Gyeonggi-do Republic of Korea
| | - Jaesik Choi
- Graduate School of Artificial Intelligence KAIST Daejeon: 291 Daehak-ro, N24, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Yousung Jung
- Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea
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41
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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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42
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Xie X, Clark Spotte-Smith EW, Wen M, Patel HD, Blau SM, Persson KA. Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network. J Am Chem Soc 2021; 143:13245-13258. [PMID: 34379977 DOI: 10.1021/jacs.1c05807] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Interfacial reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid-electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a reaction network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH2)O-C(═O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.
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Affiliation(s)
- Xiaowei Xie
- Department of Chemistry, University of California, Berkeley, California 94720, United States.,Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Evan Walter Clark Spotte-Smith
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.,Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States
| | - Mingjian Wen
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Hetal D Patel
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.,Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States
| | - Samuel M Blau
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Kristin A Persson
- Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States.,Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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43
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Zhao Q, Savoie BM. Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks. NATURE COMPUTATIONAL SCIENCE 2021; 1:479-490. [PMID: 38217124 DOI: 10.1038/s43588-021-00101-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 06/18/2021] [Indexed: 01/15/2024]
Abstract
Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation, but computational cost and inconsistent reaction coverage are still obstacles to exploring deep reaction networks. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straightforward modifications of the reaction enumeration, geometry initialization and transition state convergence algorithms that are common to many prediction methodologies. These components are implemented in the context of yet another reaction program (YARP), our reaction prediction package with which we report reaction discovery benchmarks for organic single-step reactions, thermal degradation of a γ-ketohydroperoxide, and competing ring-closures in a large organic molecule. Compared with recent benchmarks, YARP (re)discovers both established and unreported reaction pathways and products while simultaneously reducing the cost of reaction characterization by nearly 100-fold and increasing convergence of transition states. This combination of ultra-low cost and high reaction coverage creates opportunities to explore the reactivity of larger systems and more complex reaction networks for applications such as chemical degradation, where computational cost is a bottleneck.
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Affiliation(s)
- Qiyuan Zhao
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, USA
| | - Brett M Savoie
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, USA.
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44
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Zhao Q, Iovanac NC, Savoie BM. Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds. J Chem Inf Model 2021; 61:2798-2805. [PMID: 34032434 DOI: 10.1021/acs.jcim.1c00367] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Computational predictions of the thermodynamic properties of molecules and materials play a central role in contemporary reaction prediction and kinetic modeling. Due to the lack of experimental data and computational cost of high-level quantum chemistry methods, approximate methods based on additivity schemes and more recently machine learning are currently the only approaches capable of supplying the chemical coverage and throughput necessary for such applications. For both approaches, ring-containing molecules pose a challenge to transferability due to the nonlocal interactions associated with conjugation and strain that significantly impact thermodynamic properties. Here, we report the development of a self-consistent approach for parameterizing transferable ring corrections based on high-level quantum chemistry. The method is benchmarked against both the Pedley-Naylor-Kline experimental dataset for C-, H-, O-, N-, S-, and halogen-containing cyclic molecules and a dataset of Gaussian-4 quantum chemistry calculations. The prescribed approach is demonstrated to be superior to existing ring corrections while maintaining extensibility to arbitrary chemistries. We have also compared this ring-correction scheme against a novel machine learning approach and demonstrate that the latter is capable of exceeding the performance of physics-based ring corrections.
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Affiliation(s)
- Qiyuan Zhao
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Nicolae C Iovanac
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Brett M Savoie
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
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45
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Maeda S, Harabuchi Y. Exploring paths of chemical transformations in molecular and periodic systems: An approach utilizing force. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1538] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Satoshi Maeda
- Institute for Chemical Reaction Design and Discovery (WPI‐ICReDD), Hokkaido University Sapporo Hokkaido Japan
- Department of Chemistry, Faculty of Science Hokkaido University Sapporo Hokkaido Japan
- JST, ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project Sapporo Hokkaido Japan
- National Institute for Materials Science (NIMS) Research and Services Division of Materials Data and Integrated System (MaDIS) Tsukuba Ibaraki Japan
| | - Yu Harabuchi
- Institute for Chemical Reaction Design and Discovery (WPI‐ICReDD), Hokkaido University Sapporo Hokkaido Japan
- Department of Chemistry, Faculty of Science Hokkaido University Sapporo Hokkaido Japan
- JST, ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project Sapporo Hokkaido Japan
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46
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Hirai H, Jinnouchi R. Discovering chemical reaction pathways using accelerated molecular dynamics simulations and network analysis tools – Application to oxidation induced decomposition of ethylene carbonate. Chem Phys Lett 2021. [DOI: 10.1016/j.cplett.2021.138439] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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47
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Takayanagi T. Application of Reaction Path Search Calculations to Potential Energy Surface Fits. J Phys Chem A 2021; 125:3994-4002. [PMID: 33915053 DOI: 10.1021/acs.jpca.1c01512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
There has been significant progress in recent years in the use of machine learning techniques to model high-dimensional reactive potential energy surfaces using large-scale data obtained from ab initio electronic structure calculations. In these methods, the strategy used to gather data becomes a key issue as the molecular size increases. In this work, we examine the applicability of the reaction path search algorithm implemented in the Global Reaction Route Mapping (GRRM) code as a data-gathering approach. The electronic energies and gradients sampled by using the GRRM calculation are directly used in potential energy surface fitting to a permutationally invariant polynomial function. This simple approach was applied to the HNS and HCNO reaction systems, and we found that the fitted potential energy surfaces reasonably reproduce the features of the electronic structure calculations used in the GRRM calculations. This suggests that the GRRM sampling scheme can be used to construct an initial potential energy surface.
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Affiliation(s)
- Toshiyuki Takayanagi
- Department of Chemistry, Saitama University, Shimo-Okubo 255, Saitama City, Saitama 338-8570, Japan
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Stuyver T, Shaik S. Promotion Energy Analysis Predicts Reaction Modes: Nucleophilic and Electrophilic Aromatic Substitution Reactions. J Am Chem Soc 2021; 143:4367-4378. [PMID: 33689334 DOI: 10.1021/jacs.1c00307] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
To develop an approach to pre-emptively predict the existence of major reaction modes associated with a chemical system, based on exclusive consideration of reactant properties, we build herein on the valence bond perspective of chemical reactivity. In this perspective, elementary chemical reactions are conceptualized as crossovers between individual diabatic/semilocalized states. As demonstrated, the spacings between the main diabatic states in the reactant geometries-the so-called promotion energies-contain predictive information about which types of crossings are likely to occur on a potential energy surface, facilitating the identification of potential transition states and products. As an added bonus, promotion energy analysis provides direct insight into the impact of environmental effects, e.g., the presence of (polar) solvents and/or (local) electric fields, on a mechanistic landscape. We illustrate the usefulness of our approach by focusing on model nucleophilic and electrophilic aromatic substitution reactions. Overall, we envision our analysis to be useful not only as a tool for conceptualizing individual mechanistic landscapes but also as a facilitator of systematic reaction-network exploration efforts. Because the emerging VB descriptors are computationally inexpensive (and can alternatively be inferred through machine learning), they could be evaluated on-the-fly as part of an exploration algorithm. The so-predicted reaction modes could subsequently be examined in detail through computationally more-demanding methods.
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Affiliation(s)
- Thijs Stuyver
- Institute of Chemistry, The Hebrew University, Jerusalem 91904, Israel
| | - Sason Shaik
- Institute of Chemistry, The Hebrew University, Jerusalem 91904, Israel
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Robertson C, Habershon S. Simple position and orientation preconditioning scheme for minimum energy path calculations. J Comput Chem 2021; 42:761-770. [PMID: 33617652 DOI: 10.1002/jcc.26495] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/16/2021] [Accepted: 01/22/2021] [Indexed: 11/08/2022]
Abstract
Minimum-energy path (MEP) calculations, such as those typified by the nudged elastic band method, require input of reactant and product molecular configurations at initialization. In the case of reactions involving more than one molecule, generating initial reactant and product configurations requires careful consideration of the relative position and orientations of the reactive molecules in order to ensure that the resulting MEP calculation proceeds without converging on an alternative reaction-path, and without requiring excessive numbers of optimization iterations; as such, this initial system set-up is most commonly performed "by hand," with an expert user arranging reactive molecules in space to ensure that the following MEP calculation runs smoothly. In this Article, we introduce a simple preconditioning scheme which replaces this labor-intensive, human-knowledge-based step with an automated deterministic computational scheme. In our approach, initial reactant and product configurations are generated such that steric hindrance between reactive molecules is minimized in the reactant and product configurations, while also simultaneously requiring minimal structural differences between the reactants and products. The method is demonstrated using a benchmark test-set of >3400 organic molecular reactions, where comparison of the reactant/product configurations generated using our approach compare very well to initial configurations which were generated on an ad hoc basis.
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Affiliation(s)
- Christopher Robertson
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, UK
| | - Scott Habershon
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, UK
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Young TA, Silcock JJ, Sterling AJ, Duarte F. autodE: Automated Calculation of Reaction Energy Profiles— Application to Organic and Organometallic Reactions. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202011941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Tom A. Young
- Chemistry Research Laboratory University of Oxford Mansfield Road Oxford OX1 3TA UK
| | - Joseph J. Silcock
- Chemistry Research Laboratory University of Oxford Mansfield Road Oxford OX1 3TA UK
| | - Alistair J. Sterling
- Chemistry Research Laboratory University of Oxford Mansfield Road Oxford OX1 3TA UK
| | - Fernanda Duarte
- Chemistry Research Laboratory University of Oxford Mansfield Road Oxford OX1 3TA UK
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