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Particle tracking is commonly used to study time-dependent behavior in manydifferent types of physical and chemical systems involving constituents thatspan many length scales, including atoms, molecules, nanoparticles, granularparticles, and even larger objects. Behaviors of interest studied usingparticle tracking information include thermodynamic phase transitions,structural transitions, protein folding, and crystallization. A commonchallenge in studies of these systems involves change detection. Change pointdetection discerns when a temporal signal undergoes a change in distribution.These changes can be local or global, instantaneous or prolonged, obvious orsubtle. Moreover, system-wide changes marking an interesting physical orchemical phenomenon (e.g. crystallization of a liquid) are often preceded byevents (e.g. pre-nucleation clusters) that are localized and can occur anywhereat anytime in the system. For these reasons, detecting events in particletrajectories generated by molecular simulation is challenging and typicallyaccomplished via ad hoc solutions unique to the behavior and system understudy. Consequently, methods for event detection lack generality, and thoseused in one field are not easily used by scientists in other fields. Here wepresent a new Python-based tool, dupin, that allows for universal eventdetection from particle trajectory data irrespective of the system details.dupin works by creating a signal representing the simulation and partitioningthe signal based on events (changes within the trajectory). This approachallows for studies where manual annotating of event boundaries would require aprohibitive amount of time. Furthermore, dupin can serve as a tool in automatedand reproducible workflows. We demonstrate the application of dupin using twoexamples and discuss its applicability to a wider class of problems.

Change Point Detection of Events in Molecular Simulations using dupin