Scalable Equilibrium Sampling with Sequential Boltzmann Generators

Understand

Scalable sampling of molecular states in thermodynamic equilibrium is a long-standing challenge in statistical physics.

• Boltzmann generators tackle this problem by pairing normalizing flows with importance sampling to obtain uncorrelated samples under the target distribution.
• In this paper, we extend the Boltzmann generator framework with two key contributions, denoting our framework Sequential Boltzmann generators (SBG).
• The first is a highly efficient Transformer-based normalizing flow operating directly on all-atom Cartesian coordinates.