Computational Study of Chemical Reactivity Using Information-Theoretic Quantities from Density Functional Reactivity Theory for Electrophilic Aromatic Substitution Reactions

2015 ◽  
Vol 119 (29) ◽  
pp. 8216-8224 ◽  
Author(s):  
Wenjie Wu ◽  
Zemin Wu ◽  
Chunying Rong ◽  
Tian Lu ◽  
Ying Huang ◽  
...  
Keyword(s):  
Density Functional ◽  
Computational Study ◽  
Chemical Reactivity ◽  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Substitution Reactions ◽  
Information Theoretic

scholarly journals Fast and Accurate Prediction of the Regioselectivity of Electrophilic Aromatic Substitution Reactions

2017 ◽  
Author(s):  
Jimmy Kromann ◽  
Jan Jensen ◽  
Monika Kruszyk ◽  
Mikkel Jessing ◽  
Morten Jørgensen
Keyword(s):  
Expert Knowledge ◽  
Chemical Reactivity ◽  
Computational Cost ◽  
Semiempirical Method ◽  
Computational Prediction ◽  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Free Energies ◽  
Substitution Reactions ◽  
Computational Resources

While computational prediction of chemical reactivity is possible it usually requires expert knowledge and there are relatively few computational tools that can be used by a bench chemist to help guide synthesis. The RegioSQM method for predicting the regioselectivity of electrophilic aromatic substitution reactions of heteroaromatic systems is presented in this paper. RegioSQM protonates all aromatic C-H carbon atoms and identifies those with the lowest free energies in chloroform using the PM3 semiempirical method as the most nucleophilic center. These positions are found to correlate qualitatively with the regiochemical outcome in a retrospective analysis of 96% of more than 525 literature examples of electrophilic aromatic halogenation reactions. The method is automated and requires only a SMILES string of the molecule of interest, which can easily be generated using chemical drawing programs such as ChemDraw. The computational cost is 1-10 minutes per molecule depending on size, using relatively modest computational resources and the method is freely available via a web server at regiosqm.org. RegioSQM should therefore be of practical use in the planning of organic synthesis.

Electrophilic aromatic substitution reactions of compounds with Craig-Möbius aromaticity

2021 ◽  
Vol 118 (39) ◽  
pp. e2102310118
Author(s):  
Yuanting Cai ◽  
Yuhui Hua ◽  
Zhengyu Lu ◽  
Qing Lan ◽  
Zuzhang Lin ◽  
...  
Keyword(s):  
Density Functional ◽  
Electronic Effect ◽  
Density Functional Theory Calculations ◽  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Substitution Reactions ◽  
Electrophilic Attack ◽  
Functional Theory ◽  
Aromatic Species ◽  
Selective Formation

Electrophilic aromatic substitution (EAS) reactions are widely regarded as characteristic reactions of aromatic species, but no comparable reaction has been reported for molecules with Craig-Möbius aromaticity. Here, we demonstrate successful EAS reactions of Craig-Möbius aromatics, osmapentalenes, and fused osmapentalenes. The highly reactive nature of osmapentalene makes it susceptible to electrophilic attack by halogens, thus osmapentalene, osmafuran-fused osmapentalene, and osmabenzene-fused osmapentalene can undergo typical EAS reactions. In addition, the selective formation of a series of halogen substituted metalla-aromatics via EAS reactions has revealed an unprecedented approach to otherwise elusive compounds such as the unsaturated cyclic chlorirenium ions. Density functional theory calculations were conducted to study the electronic effect on the regioselectivity of the EAS reactions.

Electrophilic Addition of Brominated Poly(Isobutylene-co-4-Methylstyrene) to Olefins Catalyzed by Zinc Salts: A Model Study

1993 ◽  
Vol 66 (1) ◽  
pp. 98-108 ◽  
Author(s):  
J. M. J. Fréchet ◽  
R. Bielski ◽  
H-C. Wang ◽  
J. V. Fusco ◽  
K. W. Powers
Keyword(s):  
Induction Period ◽  
Chemical Reactivity ◽  
Zinc Stearate ◽  
Gas Chromatography Mass Spectrometry ◽  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Substitution Reactions ◽  
Model Study ◽  
Zinc Salts ◽  
Electrophilic Additions

Abstract The chemical reactivity of a new elastomer based on brominated poly(isobutylene-co-4-methylstyrene) in electrophilic additions to olefins has been investigated using model compounds as well as appropriate polymers. The reactions catalyzed by zinc salts are influenced by the solubility as well as the composition of the catalyst. While the reactivity of zinc bromide is limited by its low solubility in nonpolar medium, zinc oxide and zinc stearate can afford excellent results once an induction period has elapsed. The induction period likely corresponds to the formation of more reactive zinc based moieties through interchange reactions with the benzylic bromide groups. The mechanism of the addition process involves initial formation of carbocationic complexes with the zinc salts, followed by addition to the double bonds of the olefins. The products resulting from these additions have been characterized by NMR as well as gas chromatography-mass spectrometry. A comparison of potential crosslinking processes involving olefin addition or electrophilic aromatic substitution reactions shows that the former process is preferred. The findings of this study are directly applicable to the co-curing of elastomers based on brominated poly-(isobutylene-co-4-methylstyrene) with polyolefins.

scholarly journals Teaching a neural network to attach and detach electrons from molecules

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Roman Zubatyuk ◽  
Justin S. Smith ◽  
Benjamin T. Nebgen ◽  
Sergei Tretiak ◽  
Olexandr Isayev
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Chemical Reactivity ◽  
Closed Shell ◽  
Machine Learning Algorithms ◽  
Electrophilic Aromatic Substitution ◽  
Interatomic Potentials ◽  
Aromatic Substitution ◽  
Conceptual Density Functional Theory ◽  
Fukui Functions

AbstractInteratomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2–3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions.

scholarly journals Fast and Accurate Prediction of the Regioselectivity of Electrophilic Aromatic Substitution Reactions

2017 ◽  
Author(s):  
Jimmy Kromann ◽  
Jan Jensen ◽  
Monika Kruszyk ◽  
Mikkel Jessing ◽  
Morten Jørgensen
Keyword(s):  
Expert Knowledge ◽  
Chemical Reactivity ◽  
Computational Cost ◽  
Semiempirical Method ◽  
Computational Prediction ◽  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Free Energies ◽  
Substitution Reactions ◽  
Computational Resources

While computational prediction of chemical reactivity is possible it usually requires expert knowledge and there are relatively few computational tools that can be used by a bench chemist to help guide synthesis. The RegioSQM method for predicting the regioselectivity of electrophilic aromatic substitution reactions of heteroaromatic systems is presented in this paper. RegioSQM protonates all aromatic C-H carbon atoms and identifies those with the lowest free energies in chloroform using the PM3 semiempirical method as the most nucleophilic center. These positions are found to correlate qualitatively with the regiochemical outcome in a retrospective analysis of 96% of more than 525 literature examples of electrophilic aromatic halogenation reactions. The method is automated and requires only a SMILES string of the molecule of interest, which can easily be generated using chemical drawing programs such as ChemDraw. The computational cost is 1-10 minutes per molecule depending on size, using relatively modest computational resources and the method is freely available via a web server at regiosqm.org. RegioSQM should therefore be of practical use in the planning of organic synthesis.

Unveiling the Regioselectivity in Electrophilic Aromatic Substitution Reactions of Deactivated Benzenes through Molecular Electron Density Theory

2021 ◽  
Author(s):  
Luis R. Domingo ◽  
Mar Ríos-Gutiérrez ◽  
María José Aurell
Keyword(s):  
Electron Density ◽  
Electrophilic Aromatic Substitution ◽  
Benzene Derivatives ◽  
Aromatic Substitution ◽  
Substitution Reactions ◽  
Molecular Electron ◽  
Molecular Electron Density Theory

The origin of the meta regioselectivity in electrophilic aromatic substitution (EAS) reactions of deactivated benzene derivatives is herein analysed through Molecular Electron Density Theory (MEDT). To this end, the EAS...

scholarly journals RegioML: Predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning

2021 ◽  
Author(s):  
Nicolai Ree ◽  
Andreas H. Göller ◽  
Jan H. Jensen
Keyword(s):  
Machine Learning ◽  
Tight Binding ◽  
Reaction Centers ◽  
Gradient Boosting ◽  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Substitution Reactions ◽  
Test Set ◽  
Light Gradient ◽  
Out Of Sample

We present RegioML, an atom-based machine learning model for predicting the regioselectivities of electrophilic aromatic substitution reactions. The model relies on CM5 atomic charges computed using semiempirical tight binding (GFN1-xTB) combined with the ensemble decision tree variant light gradient boosting machine (LightGBM). The model is trained and tested on 21,201 bromination reactions with 101K reaction centers, which is split into a training, test, and out-of-sample datasets with 58K, 15K, and 27K reaction centers, respectively. The accuracy is 93% for the test set and 90% for the out-of-sample set, while the precision (the percentage of positive predictions that are correct) is 88% and 80%, respectively. The test-set performance is very similar to the graph-based WLN method developed by Struble et al. (React. Chem. Eng. 2020, 5, 896) though the comparison is complicated by the possibility that some of the test and out-of-sample molecules are used to train WLN. RegioML out-performs our physics-based RegioSQM20 method (J. Cheminform. 2021, 13:10) where the precision is only 75%. Even for the out-of-sample dataset, RegioML slightly outperforms RegioSQM20. The good performance of RegioML and WLN is in large part due to the large datasets available for this type of reaction. However, for reactions where there is little experimental data, physics-based approaches like RegioSQM20 can be used to generate synthetic data for model training. We demonstrate this by showing that the performance of RegioSQM20 can be reproduced by a ML-model trained on RegioSQM20-generated data.

Homoazulene electrophilic aromatic substitution reactions. Parallels to the chemistry of azulene

1989 ◽  
Vol 30 (3) ◽  
pp. 305-308 ◽  
Author(s):  
Lawrence T. Scott ◽  
Chris A. Sumpter ◽  
Mitsunori Oda ◽  
Ihsan Erden
Keyword(s):  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Substitution Reactions
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