Year: 2021
Communications in Computational Physics, Vol. 29 (2021), Iss. 2 : pp. 606–627
Abstract
Based on Transition Path Theory (TPT) for Markov jump processes [1, 2], we develop a general approach for identifying and calculating Transition States (TS) of stochastic chemical reacting networks. We first extend the concept of probability current, originally defined on edges connecting different nodes in the configuration space [2], to each sub-network. To locate sub-networks with maximal probability current on the separatrix between reactive and non-reactive events, which will give the Transition States of the reaction, constraint optimization is conducted. We further introduce an alternative scheme to compute the transition pathways by topological sorting, which is shown to be highly efficient through analysis.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/cicp.OA-2019-0014
Communications in Computational Physics, Vol. 29 (2021), Iss. 2 : pp. 606–627
Published online: 2021-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 22
Keywords: Transition path theory Markov chain reacting network.
Author Details
-
Clustering molecular energy landscapes by adaptive network embedding
Mercurio, Paula
Liu, Di
Journal of Materials Informatics, Vol. 4 (2024), Iss. 1
https://doi.org/10.20517/jmi.2023.40 [Citations: 0]