Coarse-Grained Boltzmann Generators

2026-02-11

AI Summary

This work proposes the Coarse-Grained Boltzmann Generator (CG-BG) to address the challenge of efficient and unbiased sampling from the Boltzmann distribution in large molecular systems. By integrating flow-based generative models with potential of mean force (PMF) reweighting in a coarse-grained coordinate space, CG-BG uniquely unifies coarse-grained modeling with an exact importance-sampling-based reweighting scheme. The PMF is efficiently learned via force matching, enabling accurate representation of complex solvent-mediated interactions even under highly compressed representations. This approach preserves statistical unbiasedness while dramatically enhancing scalability, offering a novel and efficient pathway for sampling large-scale molecular systems.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Boltzmann sampling
• molecular systems
• coarse-grained modeling
• equilibrium configurations
• importance sampling

Innovation

• Coarse-Grained Boltzmann Generators
• Potential of Mean Force
• Flow-based generative models
• Force matching
• Unbiased sampling