Related papers: Conditioning Normalizing Flows for Rare Event Sampling

Conditioning Normalizing Flows for Rare Event Sampling

URL: http://arxiv.org/abs/2207.14530v2
Date: Fri, 19 May 2023 13:24:55 GMT
Title: Conditioning Normalizing Flows for Rare Event Sampling
Authors: Sebastian Falkner, Alessandro Coretti, Salvatore Romano, Phillip Geissler, Christoph Dellago
Abstract summary: We propose a transition path sampling scheme based on neural-network generated configurations. We show that this approach enables the resolution of both the thermodynamics and kinetics of the transition region.
Score: 61.005334495264194
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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