Transition path sampling for non-equilibrium dynamics without predefined reaction coordinates

We develop two novel transition path sampling (TPS) algorithms for harvesting ensembles of rare event trajectories using non-equilibrium dynamics. These methods have the advantage that no predefined reaction coordinate is needed. Instead, an instantaneous reaction coordinate is based on the current path. Constituting a Monte Carlo random walk in trajectory space, the algorithms can be viewed as bridging between the original TPS methodology and the Rosenbluth based forward flux sampling methodology. We illustrate the new methods on toy models undergoing equilibrium and non-equilibrium dynamics, including an active Brownian particle system. For the latter, we find that transitions between steady states occur via states that are locally ordered but globally disordered.