The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.

近年来，全原子多层次非绝热分子动力学（NAMD）在大型现实系统中的模拟引起了很高的研究兴趣。然而，这种NAMD模拟通常会产生大量的时变高维数据，导致结果分析面临重大挑战。基于无监督机器学习（ML）方法，人们投入了大量的努力来开发用于识别光诱导反应通道和全面理解复杂分子运动的新颖且易于使用的分析工具。在这里，我们试图回顾该领域的近期进展，特别是如何使用无监督ML方法来分析轨迹基础的NAMD模拟结果。我们的目的是对这一分析协议的几个重要组成部分进行全面讨论，包括选择机器学习方法、构建分子描述符、建立分析框架及其优缺点和持续面临的挑战。