Deep learning the slow modes for rare events sampling
Deep learning the slow modes for rare events sampling
复制标题
DOI：
10.1073/pnas.2113533118
复制
发表时间：
2021-11-02
影响因子：
11.1
通讯作者：
Parrinello, Michele
中科院分区：
文献类型：
--
作者：
Bonati, Luigi;Piccini, GiovanniMaria;Parrinello, Michele
增强采样方法的发展极大地扩展了原子模拟的范围，允许使用可访问的计算资源来研究长期现象。许多这样的方法依赖于确定一组适当的集体变量。这些都是为了描述系统的模式，最慢的采样算法的作用下接近平衡。一旦确定，这些模式的平衡将通过选择的增强采样方法来加速。确定集体变量的一个有吸引力的方法是将它们与转移算子的本征函数和本征值联系起来。不幸的是，这需要事先了解系统的长期动态，而这通常是不可用的。然而，我们最近已经表明，它确实是可能的，以确定有效的集体变量开始有偏的模拟。在本文中，我们将机器学习的力量和最近开发的动态概率增强采样方法的效率应用于这种方法。其结果是一个强大的和强大的算法，给定的初始增强采样模拟试验集体变量或广义系综，提取转移算子特征函数使用神经网络模拟器，然后加速它们，以促进罕见事件的采样。为了说明这种方法的一般性，我们将其应用到几个系统，从一个小分子的构象转变到一个小蛋白的折叠和材料结晶的研究。
The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing longtime phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an appropriate set of collective variables. These are meant to describe the system's modes that most slowly approach equilibrium under the action of the sampling algorithm. Once identified, the equilibration of these modes is accelerated by the enhanced sampling method of choice. An attractive way of determining the collective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator. Unfortunately, this requires knowing the long-term dynamics of the system beforehand, which is generally not available. However, we have recently shown that it is indeed possible to determine efficient collective variables starting from biased simulations. In this paper, we bring the power of machine learning and the efficiency of the recently developed on the fly probability-enhanced sampling method to bear on this approach. The result is a powerful and robust algorithm that, given an initial enhanced sampling simulation performed with trial collective variables or generalized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then accelerates them to promote sampling of rare events. To illustrate the generality of this approach, we apply it to several systems, ranging from the conformational transition of a small molecule to the folding of a miniprotein and the study of materials crystallization.