Changes of graph structure of transition probability matrices indicate the slowest kinetic relaxations

Abstract

Graphs of the most probable transitions for a transition probability matrix, $e^{\tau K}$, i.e., the time evolution matrix of the transition rate matrix $K$ over a finite time interval $\tau$, are considered. We study how the graph structures of the most probable transitions change as functions of $\tau$, thereby elucidating that a kinetic threshold ${\tau }_g$ for the graph structures exists. Namely, for $\tau <{\tau }_g$, the number of connected graph components are constant. In contrast, for $\tau \ge {\tau }_g$, recombinations of most probable transitions over the connected graph components occur multiple times, which introduce drastic changes into the graph structures. Using an illustrative multi-funnel model, we show that the recombination patterns indicate the existence of the eigenvalues and eigenvectors of slowest relaxation modes quite precisely. We also devise an evaluation formula that enables us to correct the values of eigenvalues with high accuracy from the data of merging processes. We show that the graph-based method is valid for a wide range of kinetic systems with degenerate, as well as non-degenerate, relaxation rates.

arXiv comment8 figures

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• Mean first passage times reconstruct the slowest relaxations in potential energy landscapes of nanoclusters