Elimination-Reactions of Stilbene Dibromides. Debromination by Cyanide, Chloride, Iodide or 4-Nitrothiophenoxide Ions in Dimethylformamide

1986 ◽  
Vol 39 (4) ◽  
pp. 677 ◽  
Author(s):  
J Avraamides
Keyword(s):  
Transition State ◽  
Transition States ◽  
Product Distribution ◽  
Nucleophilic Attack ◽  
Distribution Data ◽  
Elimination Reactions ◽  
Transition State Structures

Transition state structures for debromination reactions of a series of para-substituted stilbene dibromides were evaluated from kinetic and product distribution data for the nucleophiles chloride, cyanide, iodide and 4-nitrothiophenoxide in dimethylformamide . The debrominations appear to utilize transition states in which nucleophilic attack is at bromine despite the strong carbon- nucleophilicity of some of the bases. Products are largely those derived from anti- debromination with all nucleophiles and reactivity is in the order 4-nitrothiophenoxide > cyanide > iodide > chloride.

scholarly journals Genereting Transition States of Isomerization Reactions with Deep Learning

2020 ◽  
Author(s):  
Lagnajit Pattanaik ◽  
John Ingraham ◽  
Colin Grambow ◽  
William H. Green
Keyword(s):  
Transition State ◽  
Transition States ◽  
Three Dimensional ◽  
Ground Truth ◽  
Distance Matrix ◽  
Quality Data ◽  
Quantum Chemistry Calculations ◽  
Quantitative Understanding ◽  
Speed Accuracy ◽  
Transition State Structures

Lack of quality data and difficulty generating these data hinder quantitative understanding of reaction kinetics. Specifically, conventional methods to generate transition state structures are deficient in speed, accuracy, or scope. We describe a novel method to generate three-dimensional transition state structures for isomerization reactions using reactant and product geometries. Our approach relies on a graph neural network to predict the transition state distance matrix and a least squares optimization to reconstruct the coordinates based on which entries of the distance matrix the model perceives to be important. We feed the structures generated by our algorithm through a rigorous quantum mechanics workflow to ensure the predicted transition state corresponds to the ground truth reactant and product. In both generating viable geometries and predicting accurate transition states, our method achieves excellent results. We envision workflows like this, which combine neural networks and quantum chemistry calculations, will become the preferred methods for computing chemical reactions.

Elimination reactions of stilbene dibromides. Dehydrobromination by acetate, cyanide or chloride ions in dimethylformamide

1983 ◽  
Vol 36 (9) ◽  
pp. 1705 ◽  
Author(s):  
J Avraamides ◽  
AJ Parker
Keyword(s):  
Transition State ◽  
Chloride Ion ◽  
Chloride Ions ◽  
Strong Preference ◽  
Product Analysis ◽  
Product Ratio ◽  
Elimination Reactions ◽  
Transition State Structures

Rates of dehydrobromination of a series of 4-nitro-and methoxystilbene dibromides by means of acetate, cyanide or chloride ions in dimethylformamide have been measured. A product analysis was performed which indicated a strong preference for anti elimination. Probable transition state structures utilized by each of the three nucleophiles are described. Attack by the base may be at either β-hydrogen (E2H) or Cα (E2C). The slowest reaction is with chloride ion, which also gives the highest anti/syn elimination product ratio.

scholarly journals Generating Transition States of Isomerization Reactions with Deep Learning

2020 ◽  
Author(s):  
Lagnajit Pattanaik ◽  
John Ingraham ◽  
Colin Grambow ◽  
William H. Green
Keyword(s):  
Transition State ◽  
Transition States ◽  
Three Dimensional ◽  
Ground Truth ◽  
Distance Matrix ◽  
Quality Data ◽  
Quantum Chemistry Calculations ◽  
Quantitative Understanding ◽  
Speed Accuracy ◽  
Transition State Structures

Lack of quality data and difficulty generating these data hinder quantitative understanding of reaction kinetics. Specifically, conventional methods to generate transition state structures are deficient in speed, accuracy, or scope. We describe a novel method to generate three-dimensional transition state structures for isomerization reactions using reactant and product geometries. Our approach relies on a graph neural network to predict the transition state distance matrix and a least squares optimization to reconstruct the coordinates based on which entries of the distance matrix the model perceives to be important. We feed the structures generated by our algorithm through a rigorous quantum mechanics workflow to ensure the predicted transition state corresponds to the ground truth reactant and product. In both generating viable geometries and predicting accurate transition states, our method achieves excellent results. We envision workflows like this, which combine neural networks and quantum chemistry calculations, will become the preferred methods for computing chemical reactions.

scholarly journals Generating Transition States of Isomerization Reactions with Deep Learning

2020 ◽  
Author(s):  
Lagnajit Pattanaik ◽  
John Ingraham ◽  
Colin Grambow ◽  
William H. Green
Keyword(s):  
Transition State ◽  
Transition States ◽  
Three Dimensional ◽  
Ground Truth ◽  
Distance Matrix ◽  
Quality Data ◽  
Quantum Chemistry Calculations ◽  
Quantitative Understanding ◽  
Speed Accuracy ◽  
Transition State Structures

Lack of quality data and difficulty generating these data hinder quantitative understanding of reaction kinetics. Specifically, conventional methods to generate transition state structures are deficient in speed, accuracy, or scope. We describe a novel method to generate three-dimensional transition state structures for isomerization reactions using reactant and product geometries. Our approach relies on a graph neural network to predict the transition state distance matrix and a least squares optimization to reconstruct the coordinates based on which entries of the distance matrix the model perceives to be important. We feed the structures generated by our algorithm through a rigorous quantum mechanics workflow to ensure the predicted transition state corresponds to the ground truth reactant and product. In both generating viable geometries and predicting accurate transition states, our method achieves excellent results. We envision workflows like this, which combine neural networks and quantum chemistry calculations, will become the preferred methods for computing chemical reactions.

scholarly journals TSNET: PREDICTING TRANSITION STATE STRUCTURES WITH TENSOR FIELD NETWORKS AND TRANSFER LEARNING

2021 ◽  
Author(s):  
Riley Jackson ◽  
Wenyuan Zhang ◽  
Jason Pearson
Keyword(s):  
Transition State ◽  
Reaction Kinetics ◽  
Transfer Learning ◽  
Protein Function ◽  
Tensor Field ◽  
Transition States ◽  
Catalyst Design ◽  
Molecular Structures ◽  
Transition State Structures

Transition states are among the most important molecular structures in chemistry, critical to a variety of fields such as reaction kinetics, catalyst design, and the study of protein function. However,...

Immutable and Variable Transition State Structures. A Reply

1979 ◽  
Vol 32 (11) ◽  
pp. 2361 ◽  
Author(s):  
DJ McLennan ◽  
PL Martin
Keyword(s):  
Transition State ◽  
Transition States ◽  
Solvent Mixtures ◽  
Water Solvent ◽  
Recent Suggestion ◽  
Detailed Treatment ◽  
Theoretical Reasoning ◽  
State Charge ◽  
Transition State Structures

The factual basis of, and the theoretical reasoning behind, a recent suggestion by Poh that transition state structures may or may not vary according to the severity of substituent change are critically examined in terms of currently held views. Both aspects of Poh's paper are found to be defective. Some results on the rates of solvolysis of diarylmethyl p-nitrobenzoates in ethanol/water solvent mixtures are considered. A superficial examination of the Hammett p and Winstein-Grunwald m parameters indicates that Poh's suggestion of immutable transition states is exemplified by this work, but a more detailed treatment shows that p and m are not necessarily functions of transition state charge separations alone.

An NBO-TS predictive approach for torquoselectivity of ring opening in 3-substituted cyclobutenes

2017 ◽  
Author(s):  
Arpita Yadav ◽  
Dasari L V K Prasad ◽  
Veejendra Yadav
Keyword(s):  
Transition State ◽  
Ring Opening ◽  
Natural Bond Orbital ◽  
Theoretical Calculations ◽  
Lone Pair ◽  
Product Distribution ◽  
Acceptor Substituent ◽  
Important Contributor ◽  
Predictive Approach ◽  
Donor Substituent

<p>The torquoselectivity, the inward or outward ring opening of 3-substituted cyclobutenes, is conventionally guided by the donor and/or acceptor ability of the substituent (S). It is typically predicted by estimating the respective ring opening transition state (TS) barriers. While there is no known dissent in regard to the outward rotation of electron-rich substituents from the approaches of TS calculations, the inward rotation was predicted for some electron-accepting substituents and outward for others. To address this divergence in predicting the torquoselectivity, we have used reliable orbital descriptors through natural bond orbital theoretical calculations and demonstrated that (a) interactions <i>n</i><i><sub>S</sub></i>→s*<sub>C3C4</sub> for a lone pair containing substituent, s<sub>S</sub>→s*<sub>C3C4</sub> for a s-donor substituent, s<sub>C3C4</sub>→p*<sub>S</sub> for a resonance-accepting substituent and s<sub>C3C4</sub>→s*<sub>S</sub> for a s-acceptor substituent constitute the true electronic controls of torquoselectivity, and (b) reversibility of the ring opening event is an additional important contributor to the observed product distribution.</p>

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