Coarse-Grained Boltzmann Generators

TL;DR

This paper introduces Coarse-Grained Boltzmann Generators, a new framework that combines reduced-order modeling with importance sampling to efficiently generate unbiased molecular configurations in large systems.

Contribution

It unifies coarse-grained modeling with importance sampling, using a learned potential of mean force to enable scalable, accurate sampling of complex molecular systems.

Findings

Successfully captures complex solvent-mediated interactions

Enables unbiased sampling of larger molecular systems

Uses force matching for efficient potential learning

Abstract

Sampling equilibrium molecular configurations from the Boltzmann distribution is a longstanding challenge. Boltzmann Generators (BGs) address this by combining exact-likelihood generative models with importance sampling, but their practical scalability is limited. Meanwhile, coarse-grained surrogates enable the modeling of larger systems by reducing effective dimensionality, yet often lack the reweighting process required to ensure asymptotically correct statistics. In this work, we propose Coarse-Grained Boltzmann Generators (CG-BGs), a principled framework that unifies scalable reduced-order modeling with the exactness of importance sampling. CG-BGs act in a coarse-grained coordinate space, using a learned potential of mean force (PMF) to reweight samples generated by a flow-based model. Crucially, we show that this PMF can be efficiently learned from rapidly converged data via force matching. Our results demonstrate that CG-BGs faithfully capture complex interactions mediated by explicit solvent within highly reduced representations, establishing a scalable pathway for the unbiased sampling of larger molecular systems.