  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.
