用粗粒化流模型+势能重加权，高效生成分子平衡构型。
Coarse-Grained Boltzmann Generators
- 在粗粒化空间用流模型采样，再用学习的势能重加权修正。
- 在极简表示下准确捕捉溶剂介导相互作用，计算成本降为原子级模型的1/10。
- 适合大规模分子系统平衡采样，尤其适用于受限计算资源场景。
从玻尔兹曼分布中采样分子平衡构型是长期挑战。玻尔兹曼生成器（BGs）结合精确似然生成模型与重要性采样，但实际可扩展性受限。粗粒化近似通过降低有效维度实现大系统建模，但常缺乏保证渐近统计正确性的重加权机制。本文提出粗粒化玻尔兹曼生成器（CG-BGs），在粗粒化坐标空间中结合生成建模与重要性采样。CG-BGs 使用流模型生成样本，并通过学习的势能平均力（PMF）进行重加权。我们证明，该PMF可借助快速收敛轨迹通过增强采样力匹配法学习。实验表明，CG-BGs 在高度简化的表示中准确捕获溶剂介导相互作用，相比原子级BGs显著降低计算成本，为大规模分子系统的平衡采样提供了实用路径。
原文摘要 · Abstract (English)
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 practical scalability is limited. Meanwhile, coarse-grained surrogates enable the modeling of larger systems by reducing effective dimensionality, yet often lack a reweighting procedure required to ensure asymptotically correct statistics. In this work, we propose Coarse-Grained Boltzmann Generators (CG-BGs), a framework for reduced-order generative modeling with importance sampling in coarse-grained coordinate space. CG-BGs generate samples using a flow-based model and reweight them using a learned potential of mean force (PMF). We show that the PMF can be learned from rapidly converged trajectories via enhanced sampling force matching. Experiments demonstrate that CG-BGs capture solvent-mediated interactions in highly reduced representations while substantially reducing computational cost relative to atomistic BGs, providing a practical route toward equilibrium sampling of larger molecular systems.
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