提出新方法提升分子系统采样效率，避免模式坍缩。
Learning Boltzmann Generators via Constrained Mass Transport
- 用约束质量传输框架控制采样过程中的分布重叠
- 在标准测试和最大规模肽类系统上有效样本量提升2.5倍以上
- 适合需要高质量采样的分子模拟与生成建模研究者
从高维多模态非归一化概率分布中高效采样是科学与机器学习的核心挑战。本文聚焦玻尔兹曼生成器（Boltzmann Generators, BGs），用于在给定温度下采样物理系统（如分子）的玻尔兹曼分布。传统变分方法最小化反向KL散度易导致模式坍缩，而基于退火的方法常依赖几何调度，存在质量跳跃问题且调参复杂。本文提出约束质量传输（Constrained Mass Transport, CMT），一种变分框架，在每一步中间分布上同时约束KL散度与熵衰减，增强分布重叠，缓解质量跳跃并防止过早收敛。在标准BG基准及新提出的ELIL四肽系统（迄今无分子动力学样本条件下研究的最大系统）上，CMT持续优于现有先进方法，有效样本量提升超过2.5倍，且未出现模式坍缩。
原文摘要 · Abstract (English)
Efficient sampling from high-dimensional and multimodal unnormalized probability distributions is a central challenge in many areas of science and machine learning. We focus on Boltzmann generators (BGs) that aim to sample the Boltzmann distribution of physical systems, such as molecules, at a given temperature. Classical variational approaches that minimize the reverse Kullback-Leibler divergence are prone to mode collapse, while annealing-based methods, commonly using geometric schedules, can suffer from mass teleportation and rely heavily on schedule tuning. 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. These constraints enhance distributional overlap, mitigate mass teleportation, and counteract premature convergence. Across standard BG benchmarks and the here introduced ELIL tetrapeptide, the largest system studied to date without access to samples from molecular dynamics, CMT consistently surpasses state-of-the-art variational methods, achieving more than 2.5x higher effective sample size while avoiding mode collapse.
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。