Machine learning transforms how microstates are sampled.

A review is presented of the article "Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning" by Frank Noé, Jonas Köhler and others on the use of a machine learning methodology to address a problem with atomistic simulations of complex molecular systems using Boltzmann probability distributions to predict equilibrium properties.