Related papers: Generative methods for sampling transition paths in molecular dynamics

Generative methods for sampling transition paths in molecular dynamics

URL: http://arxiv.org/abs/2205.02818v1
Date: Thu, 5 May 2022 17:50:10 GMT
Title: Generative methods for sampling transition paths in molecular dynamics
Authors: Tony Leli\`evre, Genevi\`eve Robin, Inass Sekkat, Gabriel Stoltz, Gabriel Victorino Cardoso
Abstract summary: Simulating transition paths linking one metastable state to another one is difficult by direct numerical methods. We explore two approaches to more efficiently generate transition paths: sampling methods based on generative models such as variational autoencoders, and importance sampling methods based on reinforcement learning.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastability. Simulating transition paths linking one metastable state to another one is difficult by direct numerical methods. In view of the promises of machine learning techniques, we explore in this work two approaches to more efficiently generate transition paths: sampling methods based on generative models such as variational autoencoders, and importance sampling methods based on reinforcement learning.

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