Computing transition path theory quantities with trajectory stratification

Abstract

Transition path theory computes statistics from ensembles of reactive trajectories. A common strategy for sampling reactive trajectories is to control the branching and pruning of trajectories so as to enhance the sampling of low probability segments. However, it can be challenging to apply transition path theory to data from such methods because determining whether configurations and trajectory segments are part of reactive trajectories requires looking backward and forward in time. Here, we show how this issue can be overcome efficiently by introducing simple data structures. We illustrate the approach in the context of nonequilibrium umbrella sampling (NEUS), but the strategy is general and can be used to obtain transition path theory statistics from other methods that sample segments of unbiased trajectories.

12 pages, 11 figures

References in corpus (3)

• PLUMED: a portable plugin for free-energy calculations with molecular dynamics
• Augmented Transition Path Theory for Sequences of Events
• Unbiased estimation of equilibrium, rates, and committors from Markov state model analysis

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• Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction
• Variational deep learning of equilibrium transition path ensembles
• Augmented Transition Path Theory for Sequences of Events
• From high-dimensional committors to reactive insights