  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.
