Title: Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling Show Chinese title

Title: 通过时间间隔邻居洗牌检测复杂分子系统中的动态域和局部波动

Abstract: Many complex molecular systems owe their properties to local dynamic rearrangements or fluctuations that, despite the rise of machine learning (ML) and sophisticated structural descriptors, remain often difficult to detect. Here we show an ML framework based on a new descriptor, named Local Environments and Neighbors Shuffling (LENS), which allows identifying dynamic domains and detecting local fluctuations in a variety of systems via tracking how much the surrounding of each molecular unit changes over time in terms of neighbor individuals. Statistical analysis of the LENS time-series data allows to blindly detect different dynamic domains within various types of molecular systems with, e.g., liquid-like, solid-like, or diverse dynamics, and to track local fluctuations emerging within them in an efficient way. The approach is found robust, versatile, and, given the abstract definition of the LENS descriptor, capable of shedding light on the dynamic complexity of a variety of (not necessarily molecular) systems. Show Chinese abstract

Abstract: 许多复杂的分子系统之所以具有特定的性质，是因为局部动态重排或涨落，尽管机器学习（ML）和复杂的结构描述符得到了发展，但这些涨落往往仍难以检测。 在这里，我们展示了一个基于新描述符——局部环境与邻居洗牌（LENS）的机器学习框架，该框架可以通过跟踪每个分子单元周围环境随时间在邻居个体方面的变化程度，来识别动态区域并检测各种系统中的局部涨落。 对LENS时间序列数据的统计分析能够盲地检测各种分子系统中的不同动态区域，例如具有类似液体、固体或多样化动力学的系统，并能以高效的方式追踪其中出现的局部涨落。 该方法被发现是稳健且多功能的，鉴于LENS描述符的抽象定义，它能够揭示各种（不一定限于分子）系统的动态复杂性。