Biasing a transition state search to locate multiple reaction pathways

2003 ◽  
Vol 118 (21) ◽  
pp. 9533-9541 ◽  
Author(s):  
Baron Peters ◽  
WanZhen Liang ◽  
Alexis T. Bell ◽  
Arup Chakraborty
Keyword(s):  
Transition State ◽  
Reaction Pathways ◽  
Transition State Search

Reactions of an Aluminium(I) Reagent with 1,2-, 1,3- and 1,5-Dienes: Dearomatisation, Reversibility, and a Pericyclic Mechanism

2019 ◽  
Author(s):  
Clare Bakewell ◽  
Martí Garçon ◽  
Richard Y Kong ◽  
Louisa O'Hare ◽  
Andrew J. P. White ◽  
...  
Keyword(s):  
Reaction Mechanism ◽  
Dft Calculations ◽  
Transition State ◽  
Reaction Pathways ◽  
Aromatic Rings ◽  
Aromatic Character ◽  
Pericyclic Reaction

The reactions of an aluminium(I) reagent with a series of 1,2-, 1,3- and 1,5-dienes are reported. In the case of 1,3-dienes the reaction occurs by a pericyclic reaction mechanism, specifically a cheletropic cycloaddition, to form aluminocyclopentene containing products. This mechanism has been interrogated by stereochemical experiments and DFT calculations. The stereochemical experiments show that the (4+1) cycloaddition follows a suprafacial topology, while calculations support a concerted albeit asynchronous pathway in which the transition state demonstrates aromatic character. Remarkably, the substrate scope of the (4+1) cycloaddition includes dienes that are either in part, or entirely, contained within aromatic rings. In these cases, reactions occur with dearomatisation of the substrate and can be reversible. In the case of 1,2- or 1,5-dienes complementary reactivity is observed; the orthogonal nature of the C=C π-bonds (1,2-diene) and the homoconjugated system (1,5-diene) both disfavour a (4+1) cycloaddition. Rather, reaction pathways are determined by an initial (2+1) cycloaddition to form an aluminocyclopropane intermediate which can in turn undergo insertion of a further C=C π-bond leading to complex organometallic products that incorporate fused hydrocarbon rings.

scholarly journals More and Faster: Simultaneously Improving Reaction Coverage and Computational Cost in Automated Reaction Prediction Tasks

2020 ◽  
Author(s):  
Qiyuan Zhao ◽  
Brett Savoie
Keyword(s):  
Transition State ◽  
Domain Knowledge ◽  
Low Cost ◽  
Computational Cost ◽  
Chemical Degradation ◽  
Reaction Pathways ◽  
Reaction Networks ◽  
Complex Reaction ◽  
Success Rates ◽  
Reaction Prediction

<div> <div> <div> <p>Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation. Although substantial progress has been made in predicting specific reaction pathways and resolving mechanisms, the computational cost and inconsistent reaction coverage of automated prediction are still obstacles to exploring deep reaction networks without using heuristics. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straight- forward modifications of the reaction enumeration, geometry initialization, and transition state convergence algorithms that are common to many emerging prediction methodologies. These changes are implemented in the context of Yet Another Reaction Program (YARP), our reaction prediction package, for which we report a head-to-head comparison with prevailing methods for two benchmark reaction prediction tasks. In all cases, we observe near perfect recapitulation of established reaction pathways and products by YARP, without the use of heuristics or other domain knowledge to guide reaction selection. In addition, YARP also discovers many new kinetically relevant pathways and products reported here for the first time. This is achieved while simultaneously reducing the cost of reaction characterization nearly 100-fold and increasing transition state success rates and intended rates over 2-fold and 10-fold, respectively, compared with recent benchmarks. This combination of ultra-low cost and high reaction-coverage creates opportunities to explore the reactivity of larger sys- tems and more complex reaction networks for applications like chemical degradation, where approaches based on domain heuristics fail. </p> </div> </div> </div>

Transition State Search Using a Guided Direct Inversion in the Iterative Subspace Method

2012 ◽  
Vol 8 (12) ◽  
pp. 5175-5179 ◽  
Author(s):  
Joseph W. May ◽  
Jeremy D. Lehner ◽  
Michael J. Frisch ◽  
Xiaosong Li
Keyword(s):  
Transition State ◽  
Subspace Method ◽  
Direct Inversion ◽  
Transition State Search

scholarly journals Exploration of Transition State Search in Physical Chemistry

Daxue Huaxue ◽  
2021 ◽  
Vol 0 (0) ◽  
pp. 2012018-0
Author(s):  
Xin Zhang ◽  
Peinan He
Keyword(s):  
Physical Chemistry ◽  
Transition State ◽  
Transition State Search

Reactions of an Aluminium(I) Reagent with 1,2-, 1,3- and 1,5-Dienes: Dearomatisation, Reversibility, and a Pericyclic Mechanism

2019 ◽  
Author(s):  
Clare Bakewell ◽  
Martí Garçon ◽  
Richard Y Kong ◽  
Louisa O'Hare ◽  
Andrew J. P. White ◽  
...  
Keyword(s):  
Reaction Mechanism ◽  
Dft Calculations ◽  
Transition State ◽  
Reaction Pathways ◽  
Aromatic Rings ◽  
Aromatic Character ◽  
Pericyclic Reaction

The reactions of an aluminium(I) reagent with a series of 1,2-, 1,3- and 1,5-dienes are reported. In the case of 1,3-dienes the reaction occurs by a pericyclic reaction mechanism, specifically a cheletropic cycloaddition, to form aluminocyclopentene containing products. This mechanism has been interrogated by stereochemical experiments and DFT calculations. The stereochemical experiments show that the (4+1) cycloaddition follows a suprafacial topology, while calculations support a concerted albeit asynchronous pathway in which the transition state demonstrates aromatic character. Remarkably, the substrate scope of the (4+1) cycloaddition includes dienes that are either in part, or entirely, contained within aromatic rings. In these cases, reactions occur with dearomatisation of the substrate and can be reversible. In the case of 1,2- or 1,5-dienes complementary reactivity is observed; the orthogonal nature of the C=C π-bonds (1,2-diene) and the homoconjugated system (1,5-diene) both disfavour a (4+1) cycloaddition. Rather, reaction pathways are determined by an initial (2+1) cycloaddition to form an aluminocyclopropane intermediate which can in turn undergo insertion of a further C=C π-bond leading to complex organometallic products that incorporate fused hydrocarbon rings.

Application of numerical basis sets with a family of DFT methods to predict the transition states of Diels-Alder reactions: Assessing the accuracy through synchronous transit method

2017 ◽  
Author(s):  
Saurav Dutta ◽  
Bhabani S. Mallik
Keyword(s):  
Transition State ◽  
Alder Reaction ◽  
Diels Alder ◽  
Computational Method ◽  
Basis Set ◽  
Basis Sets ◽  
Gaussian Basis Sets ◽  
Dft Methods ◽  
Diels Alder Reaction ◽  
Transition State Search

<div> <table> <tr> <td> <p>Knowledge of the transition state is crucial in determining the mechanism in order to diversify the applicability of the reaction. The computational method is the most convenient way to locate the transition state in the absence any efficient experimental technique. We have applied the method of the transition state search on the Diels-Alder reaction computationally by means of combined linear synchronous transit and quadratic synchronous transit methods. Here we have shown that, of various methods adopted, BOP functional with numerical basis set provides a computationally economical alternative to the widely used B3LYP functional with higher Gaussian basis sets in the transition state search. It can reproduce the experimental parameters like activation energy of the Diels-Alder reaction, and the calculations are much faster than the corresponding other functional based calculations.</p> </td> </tr> </table> </div>

scholarly journals More and Faster: Simultaneously Improving Reaction Coverage and Computational Cost in Automated Reaction Prediction Tasks

2020 ◽  
Author(s):  
Qiyuan Zhao ◽  
Brett Savoie
Keyword(s):  
Transition State ◽  
Domain Knowledge ◽  
Low Cost ◽  
Computational Cost ◽  
Chemical Degradation ◽  
Reaction Pathways ◽  
Reaction Networks ◽  
Complex Reaction ◽  
Success Rates ◽  
Reaction Prediction

<div> <div> <div> <p>Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation. Although substantial progress has been made in predicting specific reaction pathways and resolving mechanisms, the computational cost and inconsistent reaction coverage of automated prediction are still obstacles to exploring deep reaction networks without using heuristics. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straight- forward modifications of the reaction enumeration, geometry initialization, and transition state convergence algorithms that are common to many emerging prediction methodologies. These changes are implemented in the context of Yet Another Reaction Program (YARP), our reaction prediction package, for which we report a head-to-head comparison with prevailing methods for two benchmark reaction prediction tasks. In all cases, we observe near perfect recapitulation of established reaction pathways and products by YARP, without the use of heuristics or other domain knowledge to guide reaction selection. In addition, YARP also discovers many new kinetically relevant pathways and products reported here for the first time. This is achieved while simultaneously reducing the cost of reaction characterization nearly 100-fold and increasing transition state success rates and intended rates over 2-fold and 10-fold, respectively, compared with recent benchmarks. This combination of ultra-low cost and high reaction-coverage creates opportunities to explore the reactivity of larger sys- tems and more complex reaction networks for applications like chemical degradation, where approaches based on domain heuristics fail. </p> </div> </div> </div>

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