  Computing equilibrium states in condensed-matter many-body systems, such as
solvated proteins, is a long-standing challenge. Lacking methods for generating
statistically independent equilibrium samples in "one shot", vast computational
effort is invested for simulating these system in small steps, e.g., using
Molecular Dynamics. Combining deep learning and statistical mechanics, we here
develop Boltzmann Generators, that are shown to generate unbiased one-shot
equilibrium samples of representative condensed matter systems and proteins.
Boltzmann Generators use neural networks to learn a coordinate transformation
of the complex configurational equilibrium distribution to a distribution that
can be easily sampled. Accurate computation of free energy differences and
discovery of new configurations are demonstrated, providing a statistical
mechanics tool that can avoid rare events during sampling without prior
knowledge of reaction coordinates.
