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,...

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.

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.

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.

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 Popular Integration Grids Can Result in Large Errors in DFT-Computed Free Energies

2019 ◽  
Author(s):  
Andrea N. Bootsma ◽  
Steven Wheeler
Keyword(s):  
Transition State ◽  
Molecular Orientation ◽  
Density Functional ◽  
Basis Sets ◽  
Free Energies ◽  
Functional Theory ◽  
Metal Free ◽  
Stereoselective Reactions ◽  
Functionalization Reaction ◽  
Transition State Structures

<div>Density functional theory (DFT) has emerged as a powerful tool for analyzing organic and organometallic systems and proved remarkably accurate in computing the small free energy differences that underpin many chemical phenomena (e.g. regio- and stereoselective reactions). We show that the lack of rotational invariance of popular DFT integration grids reveals large uncertainties in computed free energies for isomerizations, torsional barriers, and regio- and stereoselective reactions. The result is that predictions based on DFT-computed free energies for many systems can change qualitatively depending on molecular orientation. For example, for a metal-free propargylation of benzaldehyde, predicted enantioselectivities based on B97-D/def2-TZVP free energies using the popular (75,302) integration grid can vary from 62:38 to 99:1 by simply rotating the transition state structures. Relative free energies for the regiocontrolling transition state structures for an Ir-catalyzed C–H functionalization reaction computed using M06/6-31G(d,p)/LANL2DZ and the same grid can vary by more than 5 kcal mol–1, resulting in predicted regioselectivities that range anywhere from 14:86 to >99:1. Errors of these magnitudes occur for different functionals and basis sets, are widespread among modern applications of DFT, and can be reduced by using much denser integration grids than commonly employed.</div>

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