Hare Stephanie R, Bratholm Lars A, Glowacki David R, Carpenter Barry K
University of Bristol School of Chemistry , Cantock's Close , Bristol , UK BS8 1TS.
University of Bristol School of Mathematics , University Walk , Bristol , UK BS8 1TW.
Chem Sci. 2019 Sep 18;10(43):9954-9968. doi: 10.1039/c9sc02742d. eCollection 2019 Nov 21.
Most chemical transformations (reactions or conformational changes) that are of interest to researchers have many degrees of freedom, usually too many to visualize without reducing the dimensionality of the system to include only the most important atomic motions. In this article, we describe a method of using Principal Component Analysis (PCA) for analyzing a series of molecular geometries (, a reaction pathway or molecular dynamics trajectory) and determining the reduced dimensional space that captures the most structural variance in the fewest dimensions. The software written to carry out this method is called , which permits (1) visualizing the geometries in a reduced dimensional space, (2) determining the axes that make up the reduced dimensional space, and (3) projecting the series of geometries into the low-dimensional space for visualization. We investigated two options to represent molecular structures within : aligned Cartesian coordinates and matrices of interatomic distances. We found that interatomic distance matrices better captured non-linear motions in a smaller number of dimensions. To demonstrate the utility of , we have carried out a number of applications where we have projected molecular dynamics trajectories into a reduced dimensional space defined by an intrinsic reaction coordinate. The visualizations provided by this analysis show that dynamic paths can differ greatly from the minimum energy pathway on a potential energy surface. Viewing intrinsic reaction coordinates and trajectories in this way provides a quick way to gather qualitative information about the pathways trajectories take relative to a minimum energy path. Given that the outputs from PCA are linear combinations of the input molecular structure coordinates (, Cartesian coordinates or interatomic distances), they can be easily transferred to other types of calculations that require the definition of a reduced dimensional space (, biased molecular dynamics simulations).
研究人员感兴趣的大多数化学转变(反应或构象变化)都有许多自由度,通常自由度太多,若不降低系统维度以仅包含最重要的原子运动,就难以可视化。在本文中,我们描述了一种使用主成分分析(PCA)来分析一系列分子几何结构(即反应路径或分子动力学轨迹)并确定能在最少维度中捕获最大结构变化的降维空间的方法。为实施此方法编写的软件名为[软件名称未给出],它允许:(1)在降维空间中可视化几何结构;(2)确定构成降维空间的轴;(3)将一系列几何结构投影到低维空间进行可视化。我们研究了在[软件名称未给出]中表示分子结构的两种选项:对齐的笛卡尔坐标和原子间距离矩阵。我们发现原子间距离矩阵能在更少维度中更好地捕获非线性运动。为证明[软件名称未给出]的实用性,我们进行了一些应用,将分子动力学轨迹投影到由内禀反应坐标定义的降维空间中。此分析提供的可视化结果表明,动力学路径可能与势能面上的最小能量路径有很大差异。以这种方式查看内禀反应坐标和轨迹提供了一种快速获取有关轨迹相对于最小能量路径所走路径的定性信息的方法。鉴于PCA的输出是输入分子结构坐标(即笛卡尔坐标或原子间距离)的线性组合,它们可以很容易地转移到其他需要定义降维空间的计算类型(如偏置分子动力学模拟)中。