No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
Communication: Analysing kinetic transition networks for rare events
12. J. R. Norris, Markov Chains (Cambridge University Press, 1997).
16. D. Landau and K. Binder, A Guide to Monte Carlo Simulations in Statistical Physics (Cambridge University Press, New York, 2005).
19. D. J. Wales, Energy Landscapes (Cambridge University Press, Cambridge, 2003).
21. E. Jones et al., SciPy: Open source scientific tools for Python, 2001.
23. Y. Chen, T. A. Davis, W. W. Hager, and S. Rajamanickam, ACM Trans. Math. Software 35, 22 (2009).
Article metrics loading...
The graph transformation approach is a recently proposed method for computing mean first passage times, rates, and committor probabilities for kinetic transition networks. Here we compare the performance to existing linear algebra methods, focusing on large, sparse networks. We show that graph transformation provides a much more robust framework, succeeding when numerical precision issues cause the other methods to fail completely. These are precisely the situations that correspond to rare event dynamics for which the graph transformation was introduced.
Full text loading...
Most read this month