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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