Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA.
J Chem Phys. 2013 Nov 14;139(18):184109. doi: 10.1063/1.4828457.
Finding informative low-dimensional descriptions of high-dimensional simulation data (like the ones arising in molecular dynamics or kinetic Monte Carlo simulations of physical and chemical processes) is crucial to understanding physical phenomena, and can also dramatically assist in accelerating the simulations themselves. In this paper, we discuss and illustrate the use of nonlinear intrinsic variables (NIV) in the mining of high-dimensional multiscale simulation data. In particular, we focus on the way NIV allows us to functionally merge different simulation ensembles, and different partial observations of these ensembles, as well as to infer variables not explicitly measured. The approach relies on certain simple features of the underlying process variability to filter out measurement noise and systematically recover a unique reference coordinate frame. We illustrate the approach through two distinct sets of atomistic simulations: a stochastic simulation of an enzyme reaction network exhibiting both fast and slow time scales, and a molecular dynamics simulation of alanine dipeptide in explicit water.
发现高维模拟数据（如分子动力学或物理和化学过程的动力学蒙特卡罗模拟中出现的数据）的信息丰富的低维描述对于理解物理现象至关重要，并且还可以极大地加速模拟本身。在本文中，我们讨论并说明了在挖掘高维多尺度模拟数据时使用非线性固有变量（NIV）的方法。特别是，我们关注 NIV 允许我们如何在功能上合并不同的模拟集合以及这些集合的不同部分观察结果，以及推断未明确测量的变量的方式。该方法依赖于基础过程可变性的某些简单特征来滤除测量噪声并系统地恢复唯一的参考坐标系。我们通过两组不同的原子模拟来说明该方法：一个表现出快时间尺度和慢时间尺度的酶反应网络的随机模拟，以及一个在显式水中的丙氨酸二肽的分子动力学模拟。
