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

Frank Noe, Simon Olsson, Jonas Köhler, Hao Wu

Abstract:

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 systems in small steps, e.g., using molecular dynamics. Combining deep
learning and statistical mechanics, we developed Boltzmann generators, which 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.