2015
DOI: 10.1002/jcc.23910
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Using bonding to guide transition state optimization

Abstract: Optimization of a transition state typically requires both a good initial guess of the molecular structure and one or more computationally demanding Hessian calculations to converge reliably. Often, the transition state being optimized corresponds to the barrier in a chemical reaction where bonds are being broken and formed. Utilizing the geometries and bonding information for reactants and products, an algorithm is outlined to reliably interpolate an initial guess for the transition state geometry. Additional… Show more

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Cited by 18 publications
(30 citation statements)
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“…The algorithms designed to locate TSs and RPs are usually classified as single‐ended or double‐ended . Single‐ended methods start from a single initial state and refine it systematically to locate a TS.…”
Section: Introductionmentioning
confidence: 99%
“…The algorithms designed to locate TSs and RPs are usually classified as single‐ended or double‐ended . Single‐ended methods start from a single initial state and refine it systematically to locate a TS.…”
Section: Introductionmentioning
confidence: 99%
“…From a set of benchmark reactions provided by Vaucher and Reiher, 21 adapted from Birkholz and Schlegel, 22 we took the 10 intermolecular reactions to demonstrate the results of applying our approach to a variety of different types of reaction classes. The structures for the original datasets were, in turn, obtained from a variety of sources; in some cases, product structures were obtained by geometry optimization of appropriately modified reactant structures using graphical user interface programs, 28 while other examples were obtained from previous benchmark sets, also used for TS optimization algorithms, 29 which were also generally constructed “by hand” from well‐known systems and well‐informed guesses to their TS structures.…”
Section: Resultsmentioning
confidence: 99%
“…Section 2 presents the algorithm, which optimizes a series of simple geometric functions depending on the translational and rotational degrees of freedom (DOF) of the reactive molecules. Section 3 highlights the result of each stage of our algorithm, demonstrating application of our approach to a set of 10 reactions from a benchmark set devised by Birkholz and Schlegel 21,22 . Finally, we quantify the degree of reliability of our algorithm by generating initial reactant/product configurations for a large test‐set of >3400 organic molecular reactions from the benchmark set devised by Grambow and coworkers 23 .…”
Section: Introductionmentioning
confidence: 99%
“…This approach can also be combined with the coupling constants used in the FVRC method in order to produce an initial path that minimizes S RIC and also travels through one or more particular geometries, such as known intermediates or guess transition state structures. In the case of bi-molecular reactions, it was previously demonstrated 22 that interpolation of the bond order for bonds being broken or formed is an effective approach for approximating the structure at the transition state, and the methods outlined in Section III can be modified to define the goal geometry only in terms of those bonds by setting n act = n cts , where n cts is the number of bonds being broken or formed, and defining the goal geometry only in terms of those bonds.…”
Section: Ric Arc Length Minimization Algorithmmentioning
confidence: 94%
“…where H is the potential energy Hessian and Tg is the product of the third derivative of the potential energy with the potential energy gradient. The Tg term was found to be unnecessary for good performance when using the focused VRC method, 22 so it is not included in the evaluation of η in this work. A positive definite shift matrix σ i µ jν is used to shift η so that it is positive definite while accounting for some of the curvilinear relationship between the LEC and the gradient…”
Section: Fvrc Methods Summarymentioning
confidence: 99%