Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, 518055 Shenzhen, China.
Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, 100871 Beijing, China.
J Chem Phys. 2020 Nov 7;153(17):174115. doi: 10.1063/5.0026836.
Molecular simulations are widely applied in the study of chemical and bio-physical problems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper, we propose a machine-learning approach to ally both strategies so that simulations on different scales can benefit mutually from their crosstalks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations through deep generative learning; in turn, FG simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method defines a variational and adaptive training objective, which allows end-to-end training of parametric molecular models using deep neural networks. Through multiple experiments, we show that our method is efficient and flexible and performs well on challenging chemical and bio-molecular systems.
分子模拟被广泛应用于化学和生物物理问题的研究。然而，原子模拟的可访问时间尺度是有限的，提取包含稀有事件的系统的平衡性质仍然具有挑战性。在这方面通常采用两种不同的策略：要么坚持原子水平并进行增强采样，要么通过利用粗粒模型来牺牲细节以提高速度。尽管这两种策略都很有前途，但如果单独采用其中任何一种策略，都会存在严重的局限性。在本文中，我们提出了一种机器学习方法来结合这两种策略，以便在不同尺度上的模拟可以相互受益于它们的交叉影响：通过深度生成学习可以从细粒度（FG）模拟中推断出准确的粗粒（CG）模型；反过来，通过 CG 模型的指导可以通过深度强化学习来增强 FG 模拟。我们的方法定义了一个变分和自适应的训练目标，该目标允许使用深度神经网络对参数分子模型进行端到端训练。通过多个实验，我们表明我们的方法高效灵活，在具有挑战性的化学和生物分子系统上表现良好。
