Automatic Clustering of Excited-State Trajectories: Application to Photoexcited Dynamics.
Automatic Clustering of Excited-State Trajectories: Application to Photoexcited Dynamics.
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DOI：
10.1021/acs.jctc.3c00776
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发表时间：
2023-09-26
影响因子：
5.5
通讯作者：
Kirrander, Adam
中科院分区：
文献类型：
--
作者：
Acheson, Kyle;Kirrander, Adam
我们引入自动聚类作为一种计算有效的工具，用于对光激发动力学模拟的轨迹进行分类和解释。轨迹被视为时间序列数据，通过归一化数据的方差映射选择聚类特征。提出了 L2 范数和动态时间规整作为计算距离矩阵的合适相似性度量，并且使用基于无监督密度的 DBSCAN 算法对这些矩阵进行聚类。轮廓系数和被分类为噪声的轨迹数量用作聚类的质量度量。在与 1,3-环己二烯的光化学开环反应相对应的轨迹上证明了聚类提供快速概述大型复杂轨迹数据集的能力及其在提取化学和物理见解方面的实用性，并指出聚类可用于以无偏方式生成降维表示。
We introduce automatic clustering as a computationally efficient tool for classifying and interpreting trajectories from simulations of photo-excited dynamics. Trajectories are treated as time-series data, with the features for clustering selected by variance mapping of normalized data. The L2-norm and dynamic time warping are proposed as suitable similarity measures for calculating the distance matrices, and these are clustered using the unsupervised density-based DBSCAN algorithm. The silhouette coefficient and the number of trajectories classified as noise are used as quality measures for the clustering. The ability of clustering to provide rapid overview of large and complex trajectory data sets, and its utility for extracting chemical and physical insight, is demonstrated on trajectories corresponding to the photochemical ring-opening reaction of 1,3-cyclohexadiene, noting that the clustering can be used to generate reduced dimensionality representations in an unbiased manner.
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