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.

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.

Chlorine isotope effects in the solvolysis of substituted 1-phenylethyl chlorides

1986 ◽  
Vol 64 (6) ◽  
pp. 1201-1205 ◽  
Author(s):  
Duncan J. McLennan ◽  
Allan R. Stein ◽  
Brian Dobson
Keyword(s):  
Transition State ◽  
Theoretical Calculations ◽  
Isotope Effects ◽  
Chloride Ion ◽  
Solvent Composition ◽  
Solvent Mixtures ◽  
Chlorine Isotope ◽  
Water Solvent ◽  
Alcohol Water ◽  
Substituted 1

Kinetic chlorine isotope effects attending the solvolysis of several ring-substituted 1-phenylethyl chlorides in alcohol–water solvent mixtures are reported. The k35/k37 values are insensitive to the identity of ring substituents and to solvent composition. Results are interpreted in terms of an SN1 heterolytic process incorporating a significant amount of internal return. Theoretical calculations suggest that the incipient chloride ion in the transition state may be strongly hydrogen-bonded.

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 Initial state and transition state solvation effects in the oxidation of iodide and catechol by hexachloroiridate(IV) in binary aqueous solvent mixtures

1983 ◽  
Vol 61 (7) ◽  
pp. 1361-1370 ◽  
Author(s):  
Michael J. Blandamer ◽  
John Burgess ◽  
Stephen J. Hamshere ◽  
Colin White ◽  
Robert I. Haines ◽  
...  
Keyword(s):  
Transition State ◽  
State Transition ◽  
Butyl Alcohol ◽  
Activation Process ◽  
Solvent Mixtures ◽  
Redox Systems ◽  
Initial State ◽  
Water Solvent ◽  
Aqueous Solvent ◽  
And Diffusion

Rate constants are reported for hexachloroiridate(IV) oxidation of iodide in methanol–, ethanol–, t-butyl alcohol–, ethylene glycol–, glycerol–, acetone–, acetonitrile–, and dimethyl sulphoxide – water solvent mixtures, and for the hexachloroiridate(IV) oxidation of catechol in methanol–water mixtures. With the aid of ancillary solubility data and measurements, solvent effects on reactivity have been analysed into initial state and transition state components. In the latter, there are probably both electron transfer and diffusion contributions since it is not possible to identify uniquely the separate kinetic steps associated with the activation process. In these redox systems, however, transition state solvation changes dominate. This conclusion is compared with initial state – transition state effects in the reaction of catechol with hexahydroxoantimonic acid, in the bromate oxidation of bromide, the peroxodisulphate oxidation of iodide, and with patterns established earlier for substitution at inorganic centres. In view of some disagreements and uncertainties in the literature concerning the number of molecules of water of crystallisation in potassium hexachloroiridate(III) and hexachloroiridate(IV), this matter, of considerable importance to hexachloroiridate transfer parameter estimation, is briefly reviewed.

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,...

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.

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