  Normalizing flows are generative machine learning models which can
efficiently approximate probability distributions, using only given samples of
a distribution. This architecture is used to interpolate the chiral condensate
obtained from QCD simulations with five degenerate quark flavors in the HISQ
action. From this a model for the probability distribution of the chiral
condensate as function of lattice volume, quark mass and gauge coupling is
obtained. Using the model, first order and crossover regions can be classified
and the boundary between these regions can be marked by a critical mass. An
extension of this model to studies of phase transitions in QCD with variable
number of flavors is expected to be possible.
