  Generative models are a promising tool to address the sampling problem in
multi-body and condensed-matter systems in the framework of statistical
mechanics. In this work, we show that normalizing flows can be used to learn a
transformation to map different liquid systems into each other allowing at the
same time to obtain an unbiased equilibrium distribution through a reweighting
process. Two proof-of-principles calculations are presented for the
transformation between Lennard-Jones systems of particles with different depths
of the potential well and for the transformation between a Lennard-Jones and a
system of repulsive particles. In both numerical experiments, systems are in
the liquid state. In future applications, this approach could lead to efficient
methods to simulate liquid systems at ab-initio accuracy with the computational
cost of less accurate models, such as force field or coarse-grained
simulations.
