  The generation of equilibrium samples of molecular systems has been a
long-standing problem in statistical physics. Boltzmann Generators are a
generative machine learning method that addresses this issue by learning a
transformation via a normalizing flow from a simple prior distribution to the
target Boltzmann distribution of interest. Recently, flow matching has been
employed to train Boltzmann Generators for small molecular systems in Cartesian
coordinates. We extend this work and propose a first framework for Boltzmann
Generators that are transferable across chemical space, such that they predict
zero-shot Boltzmann distributions for test molecules without being retrained
for these systems. These transferable Boltzmann Generators allow approximate
sampling from the target distribution of unseen systems, as well as efficient
reweighting to the target Boltzmann distribution. The transferability of the
proposed framework is evaluated on dipeptides, where we show that it
generalizes efficiently to unseen systems. Furthermore, we demonstrate that our
proposed architecture enhances the efficiency of Boltzmann Generators trained
on single molecular systems.
