Flows in Complex Networks: Theory, Algorithms, and Application to Lennard–Jones Cluster Rearrangement
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- 作者：Maria Cameron (1)
Eric Vanden-Eijnden (2) - 关键词：Transition path theory ; Self ; assembly ; Protein folding ; Glassy dynamics ; Markov state models
- 刊名：Journal of Statistical Physics
- 出版年：2014
- 出版时间：August 2014
- 年：2014
- 卷：156
- 期：3
- 页码：427-454
- 全文大小：
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Eric Vanden-Eijnden (2)
1. Department of Mathematics, University of Maryland, College Park, MD, 20742, USA
2. Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY, 10012, USA - ISSN：1572-9613
文摘
A set of analytical and computational tools based on transition path theory (TPT) is proposed to analyze flows in complex networks. Specifically, TPT is used to study the statistical properties of the reactive trajectories by which transitions occur between specific groups of nodes on the network. Sampling tools are built upon the outputs of TPT that allow to generate these reactive trajectories directly, or even transition paths that travel from one group of nodes to the other without making any detour and carry the same probability current as the reactive trajectories. These objects permit to characterize the mechanism of the transitions, for example by quantifying the width of the tubes by which these transitions occur, the location and distribution of their dynamical bottlenecks, etc. These tools are applied to a network modeling the dynamics of the Lennard–Jones cluster with 38 atoms ( \(\mathrm{LJ}_{38}\) ) and used to understand the mechanism by which this cluster rearranges itself between its two most likely states at various temperatures.