不依赖时间轨迹，仅用空间信息识别系统慢变变量。
Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniques
- 基于数据空间特征而非时间序列学习慢集体变量
- 可避免对长时序模拟的依赖，提升计算效率
- 适合无可用轨迹或需快速分析的复杂系统研究
理解复杂物理过程的长时间行为依赖于模式识别能力。为简化描述，常引入一组反应坐标，即集体变量（CVs）。CVs的质量直接影响对热力学与动力学的理解，尤其在原子模拟中。近年来，无监督机器学习被用于自动识别高质量CVs，但多数方法需要时间序列数据来捕捉慢动力学。本文聚焦一类新方法：仅利用数据的空间分布特性，无需输入时间轨迹即可识别对应最慢态间转换的集体变量。我们综述了该方向的最新进展，并探讨了结合热力学信息的空间学习可能路径。
原文摘要 · Abstract (English)
Understanding the long-time dynamics of complex physical processes depends on our ability to recognize patterns. To simplify the description of these processes, we often introduce a set of reaction coordinates, customarily referred to as collective variables (CVs). The quality of these CVs heavily impacts our comprehension of the dynamics, often influencing the estimates of thermodynamics and kinetics from atomistic simulations. Consequently, identifying CVs poses a fundamental challenge in chemical physics. Recently, significant progress was made by leveraging the predictive ability of unsupervised machine learning techniques to determine CVs. Many of these techniques require temporal information to learn slow CVs that correspond to the long timescale behavior of the studied process. Here, however, we specifically focus on techniques that can identify CVs corresponding to the slowest transitions between states without needing temporal trajectories as input, instead using the spatial characteristics of the data. We discuss the latest developments in this category of techniques and briefly discuss potential directions for thermodynamics-informed spatial learning of slow CVs.
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