Conditioning Normalizing Flows for Rare Event Sampling
This work addresses the challenge of efficiently sampling rare events in molecular dynamics, offering a method to improve computational efficiency and accuracy for researchers in computational chemistry and biophysics.
The authors tackled the problem of sampling rare molecular transitions by proposing a transition path sampling scheme using conditioned normalizing flows, which removes correlations between paths and enables parallelization, allowing resolution of both thermodynamics and kinetics of the transition region.
Understanding the dynamics of complex molecular processes is often linked to the study of infrequent transitions between long-lived stable states. The standard approach to the sampling of such rare events is to generate an ensemble of transition paths using a random walk in trajectory space. This, however, comes with the drawback of strong correlations between subsequently sampled paths and with an intrinsic difficulty in parallelizing the sampling process. We propose a transition path sampling scheme based on neural-network generated configurations. These are obtained employing normalizing flows, a neural network class able to generate statistically independent samples from a given distribution. With this approach, not only are correlations between visited paths removed, but the sampling process becomes easily parallelizable. Moreover, by conditioning the normalizing flow, the sampling of configurations can be steered towards regions of interest. We show that this approach enables the resolution of both the thermodynamics and kinetics of the transition region.