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

ABSTRACT: It is known that the behavior of many complex systems is controlled by local dynamic rearrangements or fluctuations occurring within them. Complex molecular systems, composed of many molecules interacting with each other in a Brownian storm, make no exception. Despite the rise of machine learning and of sophisticated structural descriptors, detecting local fluctuations and collective transitions in complex dynamic ensembles remains often difficult. Here, we show a machine learning framework based on a descriptor which we name Local Environments and Neighbors Shuffling (LENS), that allows identifying dynamic domains and detecting local fluctuations in a variety of systems in an abstract and efficient way. By tracking how much the microscopic surrounding of each molecular unit changes over t

SUBMITTER: Crippa M 

PROVIDER: S-EPMC10372573 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature