Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

Molecular dynamics or Monte Carlo methods can be used to sample equilibrium states, but these methods become computationally expensive for complex systems, where the transition from one equilibrium state to another may only occur through rare events. Noé et al. used neural networks and deep learning to generate distributions of independent soft condensed-matter samples at equilibrium. Supervised training is used to construct invertible transformations between the coordinates of the complex system of interest and simple Gaussian coordinates of the same dimensionality.