Conditional Normalizing Flows for Active Learning of Coarse-Grained
  Molecular Representations
Henrik Schopmans Pascal Friederich
Efficient sampling of the Boltzmann distribution of molecular systems is a
long-standing challenge. Recently, instead of generating long molecular
dynamics simulations, generative machine learning methods such as normalizing
flows have been used to learn the Boltzmann distribution directly, without
samples. However, this approach is susceptible to mode collapse and thus often
does not explore the full configurational space. In this work, we address this
challenge by separating the problem into two levels, the fine-grained and
coarse-grained degrees of freedom. A normalizing flow conditioned on the
coarse-grained space yields a probabilistic connection between the two levels.
To explore the configurational space, we employ coarse-grained simulations with
active learning which allows us to update the flow and make all-atom potential
energy evaluations only when necessary. Using alanine dipeptide as an example,
we show that our methods obtain a speedup to molecular dynamics simulations of
approximately 15.9 to 216.2 compared to the speedup of 4.5 of the current
state-of-the-art machine learning approach.