  Normalizing flows can transform a simple prior probability distribution into
a more complex target distribution. Here, we evaluate the ability and
efficiency of generative machine learning methods to sample the Boltzmann
distribution of an atomistic model for glass-forming liquids. This is a
notoriously difficult task, as it amounts to ergodically exploring the complex
free energy landscape of a disordered and frustrated many-body system. We
optimize a normalizing flow model to successfully transform high-temperature
configurations of a dense liquid into low-temperature ones, near the glass
transition. We perform a detailed comparative analysis with established
enhanced sampling techniques developed in the physics literature to assess and
rank the performance of normalizing flows against state-of-the-art algorithms.
We demonstrate that machine learning methods are very promising, showing a
large speedup over conventional molecular dynamics. Normalizing flows show
performances comparable to parallel tempering and population annealing, while
still falling far behind the swap Monte Carlo algorithm. Our study highlights
the potential of generative machine learning models in scientific computing for
complex systems, but also points to some of its current limitations and the
need for further improvement.
