论文标题
多尺度重新加权随机嵌入（MRSE）：集体变量的深度学习用于增强采样
Multiscale reweighted stochastic embedding (MRSE): Deep learning of collective variables for enhanced sampling
论文作者
论文摘要
机器学习方法提供了一个通用框架，用于自动查找和表示仿真数据的基本特征。此任务对于增强的采样模拟特别重要。在那里，我们寻求一些普遍的自由度，称为集体变量（CVS），以代表和推动自由能环境的采样。从理论上讲，这些简历应分离不同的亚稳态状态，并与研究的物理过程的自由度缓慢。为此，我们提出了一种新方法，我们称之为多尺度重新加权的随机嵌入（MRSE）。我们的工作建立在随机邻居嵌入的参数版本上。该技术会自动学习通过深度神经网络将高维特征空间映射到低维潜在空间的CVS。我们将几个新的进步引入了随机邻居嵌入方法，这些方法使MRSE特别适合增强采样模拟：（1）重量 - 镇压随机抽样作为具有里程碑意义的选择方案，以获得训练数据集，以在平衡表示和捕获重要的稳定状态之间取得平衡的培训数据集； （2）通过高斯混合概率模型对高维特征空间的多尺度表示； （3）重新加权程序，以说明来自偏见概率分布的培训数据。我们表明，MRSE构建了低维CV，可以正确地表征三个模型系统中不同亚稳态的状态：müller-棕色电位，丙氨酸二肽和丙氨酸四肽。
Machine learning methods provide a general framework for automatically finding and representing the essential characteristics of simulation data. This task is particularly crucial in enhanced sampling simulations. There we seek a few generalized degrees of freedom, referred to as collective variables (CVs), to represent and drive the sampling of the free energy landscape. In theory, these CVs should separate different metastable states and correspond to the slow degrees of freedom of the studied physical process. To this aim, we propose a new method that we call multiscale reweighted stochastic embedding (MRSE). Our work builds upon a parametric version of stochastic neighbor embedding. The technique automatically learns CVs that map a high-dimensional feature space to a low-dimensional latent space via a deep neural network. We introduce several new advancements to stochastic neighbor embedding methods that make MRSE especially suitable for enhanced sampling simulations: (1) weight-tempered random sampling as a landmark selection scheme to obtain training data sets that strike a balance between equilibrium representation and capturing important metastable states lying higher in free energy; (2) a multiscale representation of the high-dimensional feature space via a Gaussian mixture probability model; and (3) a reweighting procedure to account for training data from a biased probability distribution. We show that MRSE constructs low-dimensional CVs that can correctly characterize the different metastable states in three model systems: the Müller-Brown potential, alanine dipeptide, and alanine tetrapeptide.
