Literature DB >> 16292895

Transition state theory: variational formulation, dynamical corrections, and error estimates.

Eric Vanden-Eijnden1, Fabio A Tal.   

Abstract

Transition state theory (TST) is revisited, as well as evolutions upon TST such as variational TST in which the TST dividing surface is optimized so as to minimize the rate of recrossing through this surface and methods which aim at computing dynamical corrections to the TST transition rate constant. The theory is discussed from an original viewpoint. It is shown how to compute exactly the mean frequency of transition between two predefined sets which either partition phase space (as in TST) or are taken to be well-separated metastable sets corresponding to long-lived conformation states (as necessary to obtain the actual transition rate constants between these states). Exact and approximate criterions for the optimal TST dividing surface with minimum recrossing rate are derived. Some issues about the definition and meaning of the free energy in the context of TST are also discussed. Finally precise error estimates for the numerical procedure to evaluate the transmission coefficient kappaS of the TST dividing surface are given, and it is shown that the relative error on kappaS scales as 1/square root(kappaS) when kappaS is small. This implies that dynamical corrections to the TST rate constant can be computed efficiently if and only if the TST dividing surface has a transmission coefficient kappaS which is not too small. In particular, the TST dividing surface must be optimized upon (for otherwise kappaS is generally very small), but this may not be sufficient to make the procedure numerically efficient (because the optimal dividing surface has maximum kappaS, but this coefficient may still be very small).

Year:  2005        PMID: 16292895     DOI: 10.1063/1.2102898

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  13 in total

1.  Markov state modeling and dynamical coarse-graining via discrete relaxation path sampling.

Authors:  B Fačkovec; E Vanden-Eijnden; D J Wales
Journal:  J Chem Phys       Date:  2015-07-28       Impact factor: 3.488

2.  Theoretical study of the oxidation mechanisms of thiophene initiated by hydroxyl radicals.

Authors:  Abolfazl Shiroudi; Michael S Deleuze
Journal:  J Mol Model       Date:  2015-11-03       Impact factor: 1.810

3.  Perspective: Computer simulations of long time dynamics.

Authors:  Ron Elber
Journal:  J Chem Phys       Date:  2016-02-14       Impact factor: 3.488

4.  Protein dynamics and catalysis: the problems of transition state theory and the subtlety of dynamic control.

Authors:  J R E T Pineda; S D Schwartz
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2006-08-29       Impact factor: 6.237

5.  A new perspective on transition states: χ1 separatrix.

Authors:  Paul J Ledbetter; Cecilia Clementi
Journal:  J Chem Phys       Date:  2011-07-28       Impact factor: 3.488

6.  MDMS: Molecular Dynamics Meta-Simulator for evaluating exchange type sampling methods.

Authors:  Daniel B Smith; Asim Okur; Bernard Brooks
Journal:  Chem Phys Lett       Date:  2012-07-19       Impact factor: 2.328

Review 7.  Path Sampling Methods for Enzymatic Quantum Particle Transfer Reactions.

Authors:  M W Dzierlenga; M J Varga; S D Schwartz
Journal:  Methods Enzymol       Date:  2016-06-16       Impact factor: 1.600

8.  Electron transfer across a thermal gradient.

Authors:  Galen T Craven; Abraham Nitzan
Journal:  Proc Natl Acad Sci U S A       Date:  2016-07-22       Impact factor: 11.205

9.  Value of Temporal Information When Analyzing Reaction Coordinates.

Authors:  Piao Ma; Ron Elber; Dmitrii E Makarov
Journal:  J Chem Theory Comput       Date:  2020-09-08       Impact factor: 6.006

Review 10.  Bridging scales through multiscale modeling: a case study on protein kinase A.

Authors:  Britton W Boras; Sophia P Hirakis; Lane W Votapka; Robert D Malmstrom; Rommie E Amaro; Andrew D McCulloch
Journal:  Front Physiol       Date:  2015-09-09       Impact factor: 4.566

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.