Coarse-Grained Boltzmann Generators

• URL: http://arxiv.org/abs/2602.10637v1
• Date: Wed, 11 Feb 2026 08:37:13 GMT
• Title: Coarse-Grained Boltzmann Generators
• Authors: Weilong Chen, Bojun Zhao, Jan Eckwert, Julija Zavadlav,
• Abstract summary: We propose a principled framework that unifies scalable reduced-order modeling with the exactness of importance sampling.<n>CG-BGs act in a coarse-grained coordinate space, using a learned potential of mean force to reweight samples generated by a flow-based model.<n>Our results demonstrate that CG-BGs faithfully capture complex interactions mediated by explicit solvent within highly reduced representations.
• Score: 2.8880597165704
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• FALCON: Few-step Accurate Likelihoods for Continuous Flows [78.37361800856583]
  We propose Few-step Accurate Likelihoods for Continuous Flows (FALCON), which allows for few-step sampling with a likelihood accurate enough for importance sampling applications.<n>We show FALCON outperforms state-of-the-art normalizing flow models for molecular Boltzmann sampling and is two orders of magnitude faster than the equivalently performing CNF model.
  arXiv  Detail & Related papers  (2025-12-10T18:47:25Z)
• Learning Boltzmann Generators via Constrained Mass Transport [26.687838638430595]
  We introduce Constrained Mass Transport (CMT), a variational framework that generates intermediate distributions under constraints on both the KL divergence and the entropy decay between successive steps.<n>CMT consistently surpasses state-of-the-art variational methods, achieving more than 2.5x higher effective sample size while avoiding mode collapse.
  arXiv  Detail & Related papers  (2025-10-21T09:34:01Z)
• Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials [2.8355616606687506]
  We introduce enhanced sampling to bias along CG degrees of freedom for data generation, and then re-compute the forces with respect to the unbiased potential.<n>This strategy simultaneously shortens the simulation time required to produce equilibrated data and enriches sampling in transition regions, while preserving the correct PMF.<n>Our findings support the use of enhanced sampling for force matching as a promising direction to improve the accuracy and reliability of CGs.
  arXiv  Detail & Related papers  (2025-10-13T08:40:13Z)
• Torsional-GFN: a conditional conformation generator for small molecules [75.91029322687771]
  We introduce a conditional GFlowNet specifically designed to sample conformations of molecules proportionally to their Boltzmann distribution.<n>Our work presents a promising avenue for scaling the proposed approach to larger molecular systems.
  arXiv  Detail & Related papers  (2025-07-15T21:53:25Z)
• BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation [1.2874523233023452]
  Efficient sampling from the Boltzmann distribution is a key challenge for modeling complex physical systems such as molecules.<n>We train an energy-based model (EBM) to approximate likelihoods using both noise contrastive estimation (NCE) and score matching.<n>Our approach also exhibits effective transfer learning, generalizing to new systems at inference time and achieving at least a $6times$ speedup over standard MD.
  arXiv  Detail & Related papers  (2025-07-01T15:18:28Z)
• Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models [50.77646970127369]
  We propose an energy-based diffusion model with a Fokker--Planck-derived regularization term to enforce consistency.<n>We demonstrate our approach by sampling and simulating multiple biomolecular systems, including fast-folding proteins.
  arXiv  Detail & Related papers  (2025-06-20T16:38:29Z)
• Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data [0.0]
  Coarse-grained (CG) models provide an effective route to reducing the complexity of molecular simulations.<n>We introduce a fully data-free, generative framework for CG that directly targets the all-atom Boltzmann distribution.<n>We show that the method captures all relevant modes of the Boltzmann distribution, reconstructs atomic configurations, and automatically learns physically meaningful CG representations.
  arXiv  Detail & Related papers  (2025-04-29T17:05:27Z)
• Scalable Equilibrium Sampling with Sequential Boltzmann Generators [60.00515282300297]
  We extend the Boltzmann generator framework with two key contributions.<n>The first is a highly efficient Transformer-based normalizing flow operating directly on all-atom Cartesian coordinates.<n>In particular, we perform inference-time scaling of flow samples using a continuous-time variant of sequential Monte Carlo.
  arXiv  Detail & Related papers  (2025-02-25T18:59:13Z)
• Iterated Denoising Energy Matching for Sampling from Boltzmann Densities [109.23137009609519]
  Iterated Denoising Energy Matching (iDEM) iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our matching objective. We show that the proposed approach achieves state-of-the-art performance on all metrics and trains $2-5times$ faster.
  arXiv  Detail & Related papers  (2024-02-09T01:11:23Z)
• Continual Learning with Fully Probabilistic Models [70.3497683558609]
  We present an approach for continual learning based on fully probabilistic (or generative) models of machine learning. We propose a pseudo-rehearsal approach using a Gaussian Mixture Model (GMM) instance for both generator and classifier functionalities. We show that GMR achieves state-of-the-art performance on common class-incremental learning problems at very competitive time and memory complexity.
  arXiv  Detail & Related papers  (2021-04-19T12:26:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.