Change point detection of events in molecular simulations using dupin

Particle tracking is commonly used to study time-dependent behavior in many different types of physical and chemical systems involving constituents that span many length scales, including atoms, molecules, nanoparticles, granular particles, and even larger objects. Behaviors of interest studied using particle tracking information include disorder-order transitions, thermodynamic phase transitions, structural transitions, protein folding, crystallization, gelation, swarming, avalanches and fracture. A common challenge in studies of these systems involves change detection. Change point detection discerns when a temporal signal undergoes a change in distribution. These changes can be local or global, instantaneous or prolonged, obvious or subtle. Moreover, system-wide changes marking an interesting physical or chemical phenomenon (e.g. crystallization of a liquid) are often preceded by events (e.g. pre-nucleation clusters) that are localized and can occur anywhere at anytime in the system. For these reasons, detecting events in particle trajectories generated by molecular simulation is challenging and typically accomplished via ad hoc solutions unique to the behavior and system under study. Consequently, methods for event detection lack generality, and those used in one field are not easily used by scientists in other fields. Here we present a new Python-based tool, dupin, that allows for universal event detection from particle trajectory data irrespective of the system details. dupin works by creating a signal representing the simulation and partitioning the signal based on events (changes within the trajectory). This approach allows for studies where manual annotating of event boundaries would require a prohibitive amount of time. Furthermore, dupin can serve as a tool in automated and reproducible workflows. We demonstrate the application of dupin using three examples and discuss its applicability to a wider class of problems.

粒子追踪技术广泛应用于多种物理与化学系统的时变行为研究，这些系统的组成单元覆盖多尺度范围，涵盖原子、分子、纳米颗粒、粗颗粒乃至更大尺寸的物体。借助粒子追踪数据开展研究的目标行为包括无序-有序转变、热力学相变、结构转变、蛋白质折叠、结晶、凝胶化、集群运动、雪崩与断裂等。此类系统研究中的一项核心挑战为变化检测：变点检测旨在识别时序信号的分布发生改变的时刻。这类变化可分为局部或全局、瞬时或持续、显著或细微等不同类型。此外，标志着典型物理或化学现象（如液体结晶）的系统级变化，往往先于其发生的是局域性前驱事件（如预成核团簇）——这类事件可在系统内任意位置、任意时刻出现。基于上述原因，对分子模拟生成的粒子轨迹开展事件检测极具挑战性，目前通常只能针对具体研究对象与行为，采用定制化的特设解决方案实现。因此，现有事件检测方法通用性不足，某一领域使用的检测方法难以被其他领域的科研人员直接复用。为此，本文提出一款基于Python开发的新型工具dupin，可无需考虑系统细节，直接对粒子轨迹数据开展通用化事件检测。dupin的工作原理为：首先构建表征模拟过程的时序信号，再基于轨迹内的事件（即轨迹中的变化）对该信号进行分割。该方法可有效解决手动标注事件边界所需耗时过于高昂的研究场景。此外，dupin可作为自动化与可复现科研工作流中的得力工具。本文通过三个示例展示dupin的实际应用，并探讨其在更多类别的研究问题中的适用性。