Steered Transition Path Sampling

TL;DR

This paper presents a novel path sampling technique that efficiently estimates statistical properties of stochastic dynamics by decomposing trajectories and controlling the sampling of dynamic events, especially useful for barrier crossing and diffusive systems.

Contribution

The paper introduces a new path sampling method that decomposes trajectories and uses progress constraints to improve sampling of dynamic events, addressing limitations of shooting methods.

Findings

Effective in calculating transition probabilities in barrier crossing problems.

Accurately estimates survival probabilities in diffusive systems with absorbing states.

Relates to and improves upon existing path sampling algorithms.

Abstract

We introduce a path sampling method for obtaining statistical properties of an arbitrary stochastic dynamics. The method works by decomposing a trajectory in time, estimating the probability of satisfying a progress constraint, modifying the dynamics based on that probability, and then reweighting to calculate averages. Because the progress constraint can be formulated in terms of occurrences of events within time intervals, the method is particularly well suited for controlling the sampling of currents of dynamic events. We demonstrate the method for calculating transition probabilities in barrier crossing problems and survival probabilities in strongly diffusive systems with absorbing states, which are difficult to treat by shooting. We discuss the relation of the algorithm to other methods.