用流匹配直接采样构象变量，加速自由能面计算
FES-FM: Free Energy Surface Sampling via Reduced Flow Matching
- 在集体变量空间训练动力学传输映射，实现直接采样
- 多粒子系统基于哈密顿矩阵构建先验，保证物理合理性
- 在氨基酸二肽等体系上提速显著，适合分子模拟场景
采样集体变量（CVs）分布并估计其对应的自由能面（FES），是统计物理中的关键问题，对理解化学反应和构象转变至关重要。传统方法依赖高维构型空间的模拟，再将结果投影到CV空间。为提升采样速度，我们提出FES-FM——一种用于自由能面采样的简化流匹配（Reduced Flow Matching）方法。通过在CV空间训练动态传输映射，实现对CV分布的直接采样，并重建对应自由能面。对于多粒子系统，基于势能局部极小点的哈密顿矩阵构建先验分布，确保旋转-平移不变性与物理解释性。我们在多种势函数和集体变量上评估该方法，包括隐式溶剂中的丙氨酸二肽作为分子基准。对比实验表明，本方法显著提升采样速度，同时保持精度。
原文摘要 · Abstract (English)
Sampling the distribution of collective variables (CVs) and estimating the associated free energy surface are crucial problems in statistical physics, as they underpin a better understanding of chemical reactions and conformational transitions. Traditional methods usually rely on simulations in high-dimensional configuration space and project the resulting configurations onto the CV space. To improve sampling speed, we propose FES-FM, a reduced flow matching (FM) method for free energy surface (FES) sampling. We train a dynamical transport map in the CV space, thereby enabling direct sampling of CV distributions and reconstruction of the corresponding free energy surface. For many-particle systems, we construct a prior distribution based on the Hessian at a local minimum of the potential, which ensures both rotation-translation invariance and physically meaningful configurations. We evaluate the proposed method across a variety of potential functions and collective variables, including alanine dipeptide in implicit solvent as a molecular benchmark. Comparative experiments demonstrate that our approach significantly improves sampling speed while maintaining accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。