Efficient sampling of the Boltzmann distribution of molecular systems is a
long-standing challenge. Recently, instead of generating long molecular
dynamics simulations, generative machine learning methods such as normalizing
flows have been used to learn the Boltzmann distribution directly, without
samples. However, this approach is susceptible to mode collapse and thus often
does not explore the full configurational space. In this work, we address this
challenge by separating the problem into two levels, the fine-grained and
coarse-grained degrees of freedom. A normalizing flow conditioned on the
coarse-grained space yields a probabilistic connection between the two levels.
To explore the configurational space, we employ coarse-grained simulations with
active learning which allows us to update the flow and make all-atom potential
energy evaluations only when necessary. Using alanine dipeptide as an example,
we show that our methods obtain a speedup to molecular dynamics simulations of
approximately 15.9 to 216.2 compared to the speedup of 4.5 of the current
state-of-the-art machine learning approach.

本研究通过将问题分为精细程度和粗粒程度两个层面来解决 Boltzmann 分布的高效采样问题。利用粗粒程度空间上的条件正则流，实现了两个层面之间的概率联系，并使用主动学习的粗粒程度模拟来探索构型空间，从而在必要时更新流和进行全原子势能评估，通过丙氨酸二肽的实例表明，与当前最先进的机器学习方法相比，本方法在分子动力学仿真加速上可达到 15.9 至 216.2 倍的加速比。