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Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

Martina CrippaAnnalisa CardelliniCristina CarusoGiovanni M. Pavan
Dec 2022
摘要
Many complex molecular systems owe their properties to local dynamicrearrangements 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 LocalEnvironments and Neighbors Shuffling (LENS), which allows identifying dynamicdomains and detecting local fluctuations in a variety of systems via trackinghow much the surrounding of each molecular unit changes over time in terms ofneighbor individuals. Statistical analysis of the LENS time-series data allowsto blindly detect different dynamic domains within various types of molecularsystems with, e.g., liquid-like, solid-like, or diverse dynamics, and to tracklocal fluctuations emerging within them in an efficient way. The approach isfound robust, versatile, and, given the abstract definition of the LENSdescriptor, capable of shedding light on the dynamic complexity of a variety of(not necessarily molecular) systems.
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