分子模拟轨迹的时滞t分布随机邻域嵌入（t-SNE）
Time-Lagged t-Distributed Stochastic Neighbor Embedding (t-SNE) of Molecular Simulation Trajectories
摘要：
分子模拟轨迹表示高维数据。这些数据可以通过降维的方法可视化。由于原子的运动是非线性的，非线性降维方法可能比线性降维方法更有效。在这里，我们测试了一个流行的非线性t分布随机邻居嵌入（t-SNE）方法分析的轨迹丙氨酸二肽的动力学和色氨酸笼折叠和展开。此外，我们引入了一个时间滞后的t-SNE的变体，以专注于分子系统中的慢动作。这种时间滞后的t-SNE有效地可视化分子系统的缓慢动力学。
英文摘要：
Molecular simulation trajectories represent high-dimensional data. Such data can be visualized by methods of dimensionality reduction. Non-linear dimensionality reduction methods are likely to be more efficient than linear ones due to the fact that motions of atoms are non-linear. Here we test a popular non-linear t-distributed stochastic neighbor embedding (t-SNE) method on analysis of trajectories of alanine dipeptide dynamics and Trp-cage folding and unfolding. Furthermore, we introduced a time-lagged variant of t-SNE in order to focus on slow motions in the molecular system. This time-lagged t-SNE efficiently visualizes slow dynamics of molecular systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
