  Markov chain Monte Carlo methods are a powerful tool for sampling equilibrium
configurations in complex systems. One problem these methods often face is slow
convergence over large energy barriers. In this work, we propose a novel method
which increases convergence in systems composed of many metastable states. This
method aims to connect metastable regions directly using generative neural
networks in order to propose new configurations in the Markov chain and
optimizes the acceptance probability of large jumps between modes in
configuration space. We provide a comprehensive theory and demonstrate the
method on example systems.
