Transition path theory

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The dynamical behavior of many systems arising in physics, chemistry, biology, etc. is dominated by rare but important transition events between long lived states. For over 70 years, transition state theory (TST) has provided the main theoretical framework for the description of these events [17,33,34]. Yet, while TST and evolutions thereof based on the reactive flux formalism [1, 5] (see also [30,31]) give an accurate estimate of the transition rate of a reaction, at least in principle, the theory tells very little in terms of the mechanism of this reaction. Recent advances, such as transition path sampling (TPS) of Bolhuis, Chandler, Dellago, and Geissler [3, 7] or the action method of Elber [15, 16], may seem to go beyond TST in that respect: these techniques allow indeed to sample the ensemble of reactive trajectories, i.e. the trajectories by which the reaction occurs. And yet, the reactive trajectories may again be rather uninformative about the mechanism of the reaction. This may sound paradoxical at first: what more than actual reactive trajectories could one need to understand a reaction? The problem, however, is that the reactive trajectories by themselves give only a very indirect information about the statistical properties of these trajectories. This is similar to why statistical mechanics is not simply a footnote in books about classical mechanics. What is the probability density that a trajectory be at a given location in state-space conditional on it being reactive? What is the probability current of these reactive trajectories? What is their rate of appearance? These are the questions of interest and they are not easy to answer directly from the ensemble of reactive trajectories. The right framework to tackle these questions also goes beyond standard equilibrium statistical mechanics because of the nontrivial bias that the very definition of the reactive trajectories imply -they must be involved in a reaction. The aim of this chapter is to introduce the reader to the probabilistic framework one can use to characterize the mechanism of a reaction and obtain the probability density, current, rate, etc. of the reactive trajectories.

Original languageEnglish (US)
Title of host publicationComputer Simulations in Condensed Matter Systems: From Materials to Chemical Biology Volume 1
Pages453-493
Number of pages41
Volume703
DOIs
StatePublished - 2006

Publication series

NameLecture Notes in Physics
Volume703
ISSN (Print)00758450

Fingerprint

trajectories
statistical mechanics
classical mechanics
readers
biology
sampling
chemistry
current density
formalism
physics
acoustics
estimates

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)

Cite this

Vanden Eijnden, E. (2006). Transition path theory. In Computer Simulations in Condensed Matter Systems: From Materials to Chemical Biology Volume 1 (Vol. 703, pp. 453-493). (Lecture Notes in Physics; Vol. 703). https://doi.org/10.1007/3-540-35273-2_13

Transition path theory. / Vanden Eijnden, Eric.

Computer Simulations in Condensed Matter Systems: From Materials to Chemical Biology Volume 1. Vol. 703 2006. p. 453-493 (Lecture Notes in Physics; Vol. 703).

Research output: Chapter in Book/Report/Conference proceedingChapter

Vanden Eijnden, E 2006, Transition path theory. in Computer Simulations in Condensed Matter Systems: From Materials to Chemical Biology Volume 1. vol. 703, Lecture Notes in Physics, vol. 703, pp. 453-493. https://doi.org/10.1007/3-540-35273-2_13
Vanden Eijnden E. Transition path theory. In Computer Simulations in Condensed Matter Systems: From Materials to Chemical Biology Volume 1. Vol. 703. 2006. p. 453-493. (Lecture Notes in Physics). https://doi.org/10.1007/3-540-35273-2_13
Vanden Eijnden, Eric. / Transition path theory. Computer Simulations in Condensed Matter Systems: From Materials to Chemical Biology Volume 1. Vol. 703 2006. pp. 453-493 (Lecture Notes in Physics).
@inbook{e2132d01b2da4a718105f2cd9eb3f7d3,
title = "Transition path theory",
abstract = "The dynamical behavior of many systems arising in physics, chemistry, biology, etc. is dominated by rare but important transition events between long lived states. For over 70 years, transition state theory (TST) has provided the main theoretical framework for the description of these events [17,33,34]. Yet, while TST and evolutions thereof based on the reactive flux formalism [1, 5] (see also [30,31]) give an accurate estimate of the transition rate of a reaction, at least in principle, the theory tells very little in terms of the mechanism of this reaction. Recent advances, such as transition path sampling (TPS) of Bolhuis, Chandler, Dellago, and Geissler [3, 7] or the action method of Elber [15, 16], may seem to go beyond TST in that respect: these techniques allow indeed to sample the ensemble of reactive trajectories, i.e. the trajectories by which the reaction occurs. And yet, the reactive trajectories may again be rather uninformative about the mechanism of the reaction. This may sound paradoxical at first: what more than actual reactive trajectories could one need to understand a reaction? The problem, however, is that the reactive trajectories by themselves give only a very indirect information about the statistical properties of these trajectories. This is similar to why statistical mechanics is not simply a footnote in books about classical mechanics. What is the probability density that a trajectory be at a given location in state-space conditional on it being reactive? What is the probability current of these reactive trajectories? What is their rate of appearance? These are the questions of interest and they are not easy to answer directly from the ensemble of reactive trajectories. The right framework to tackle these questions also goes beyond standard equilibrium statistical mechanics because of the nontrivial bias that the very definition of the reactive trajectories imply -they must be involved in a reaction. The aim of this chapter is to introduce the reader to the probabilistic framework one can use to characterize the mechanism of a reaction and obtain the probability density, current, rate, etc. of the reactive trajectories.",
author = "{Vanden Eijnden}, Eric",
year = "2006",
doi = "10.1007/3-540-35273-2_13",
language = "English (US)",
isbn = "3540352708",
volume = "703",
series = "Lecture Notes in Physics",
pages = "453--493",
booktitle = "Computer Simulations in Condensed Matter Systems: From Materials to Chemical Biology Volume 1",

}

TY - CHAP

T1 - Transition path theory

AU - Vanden Eijnden, Eric

PY - 2006

Y1 - 2006

N2 - The dynamical behavior of many systems arising in physics, chemistry, biology, etc. is dominated by rare but important transition events between long lived states. For over 70 years, transition state theory (TST) has provided the main theoretical framework for the description of these events [17,33,34]. Yet, while TST and evolutions thereof based on the reactive flux formalism [1, 5] (see also [30,31]) give an accurate estimate of the transition rate of a reaction, at least in principle, the theory tells very little in terms of the mechanism of this reaction. Recent advances, such as transition path sampling (TPS) of Bolhuis, Chandler, Dellago, and Geissler [3, 7] or the action method of Elber [15, 16], may seem to go beyond TST in that respect: these techniques allow indeed to sample the ensemble of reactive trajectories, i.e. the trajectories by which the reaction occurs. And yet, the reactive trajectories may again be rather uninformative about the mechanism of the reaction. This may sound paradoxical at first: what more than actual reactive trajectories could one need to understand a reaction? The problem, however, is that the reactive trajectories by themselves give only a very indirect information about the statistical properties of these trajectories. This is similar to why statistical mechanics is not simply a footnote in books about classical mechanics. What is the probability density that a trajectory be at a given location in state-space conditional on it being reactive? What is the probability current of these reactive trajectories? What is their rate of appearance? These are the questions of interest and they are not easy to answer directly from the ensemble of reactive trajectories. The right framework to tackle these questions also goes beyond standard equilibrium statistical mechanics because of the nontrivial bias that the very definition of the reactive trajectories imply -they must be involved in a reaction. The aim of this chapter is to introduce the reader to the probabilistic framework one can use to characterize the mechanism of a reaction and obtain the probability density, current, rate, etc. of the reactive trajectories.

AB - The dynamical behavior of many systems arising in physics, chemistry, biology, etc. is dominated by rare but important transition events between long lived states. For over 70 years, transition state theory (TST) has provided the main theoretical framework for the description of these events [17,33,34]. Yet, while TST and evolutions thereof based on the reactive flux formalism [1, 5] (see also [30,31]) give an accurate estimate of the transition rate of a reaction, at least in principle, the theory tells very little in terms of the mechanism of this reaction. Recent advances, such as transition path sampling (TPS) of Bolhuis, Chandler, Dellago, and Geissler [3, 7] or the action method of Elber [15, 16], may seem to go beyond TST in that respect: these techniques allow indeed to sample the ensemble of reactive trajectories, i.e. the trajectories by which the reaction occurs. And yet, the reactive trajectories may again be rather uninformative about the mechanism of the reaction. This may sound paradoxical at first: what more than actual reactive trajectories could one need to understand a reaction? The problem, however, is that the reactive trajectories by themselves give only a very indirect information about the statistical properties of these trajectories. This is similar to why statistical mechanics is not simply a footnote in books about classical mechanics. What is the probability density that a trajectory be at a given location in state-space conditional on it being reactive? What is the probability current of these reactive trajectories? What is their rate of appearance? These are the questions of interest and they are not easy to answer directly from the ensemble of reactive trajectories. The right framework to tackle these questions also goes beyond standard equilibrium statistical mechanics because of the nontrivial bias that the very definition of the reactive trajectories imply -they must be involved in a reaction. The aim of this chapter is to introduce the reader to the probabilistic framework one can use to characterize the mechanism of a reaction and obtain the probability density, current, rate, etc. of the reactive trajectories.

UR - http://www.scopus.com/inward/record.url?scp=33947213271&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33947213271&partnerID=8YFLogxK

U2 - 10.1007/3-540-35273-2_13

DO - 10.1007/3-540-35273-2_13

M3 - Chapter

SN - 3540352708

SN - 9783540352709

VL - 703

T3 - Lecture Notes in Physics

SP - 453

EP - 493

BT - Computer Simulations in Condensed Matter Systems: From Materials to Chemical Biology Volume 1

ER -