死或生：使用监督学习区分主动与被动粒子
Dead or alive: Distinguishing active from passive particles using supervised learning
AI总结：
本研究针对密集无序物质中主动与被动布朗粒子混合物，开发了基于Voronoi镶嵌的机器学习方法，能高效准确地从局部结构信息中识别主动粒子，并揭示了动态倾向与非平衡活性的结构特征差异。
摘要：
密集无序物质领域一个长期悬而未决的问题是结构与动力学之间究竟如何精确关联。随着机器学习的出现，基于局部结构环境对密集液体中粒子的动态倾向进行无偏预测成为可能。然而，迄今为止，这些机器学习研究几乎完全聚焦于由被动粒子组成的简单液体。在此，我们考虑了被动与主动（即自驱动）布朗粒子的混合物，旨在从最少的局部结构信息中识别出主动粒子。我们将针对被动系统的最先进机器学习方法与我们基于Voronoi镶嵌开发的新方法进行了比较。两种方法均能在高活性且主动粒子浓度低的情况下，根据其结构特性准确识别主动粒子。然而，我们的Voronoi方法在训练和部署上明显更快，因为它需要更少且易于计算的输入特征。值得注意的是，当活性较低时，两者均失效，这表明动态倾向与非平衡活性在结构特征上存在根本差异。最终，这些努力也可能在生物活性玻璃（如融合细胞层）的背景下发挥作用，在这些系统中，微观结构的细微变化可预示细胞动力学中的病理变化。
英文摘要：
A longstanding open question in the field of dense disordered matter is how precisely structure and dynamics are related to each other. With the advent of machine learning, it has become possible to agnostically predict the dynamic propensity of a particle in a dense liquid based on its local structural environment. Thus far, however, these machine-learning studies have focused almost exclusively on simple liquids composed of passive particles. Here we consider a mixture of both passive and active (i.e.\ self-propelled) Brownian particles, with the aim to identify the active particles from minimal local structural information. We compare a state-of-the-art machine learning approach for passive systems with a new method we develop based on Voronoi tessellation. Both methods accurately identify the active particles based on their structural properties at high activity and low concentrations of active particles. Our Voronoi method is, however, substantially faster to train and deploy because it requires fewer, and easy to compute, input features. Notably, both become ineffective when the activity is low, suggesting a fundamentally different structural signature for dynamic propensity and non-equilibrium activity. Ultimately, these efforts might also find relevance in the context of biological active glasses such as confluent cell layers, where subtle changes in the microstructure can hint at pathological changes in cell dynamics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
