Dynamical aggregation of self-propelled colloidal particles

我们研究了人工胶体自推进粒子的最小模型的集体行为。这些“活性”颗粒被设想用于一系列应用，例如，用于药物的有效输送和具有在被动材料中不可能实现的结构和功能的非平衡材料的设计。这些模型的特点是结合了短程相互作用和定向运动，维持这种相互作用需要稳定的工作。即使这些系统是真正的不平衡，观察到的大规模集体行为往往类似于被动系统，这可以暂时解释为由于本地，自主驾驶的特点。在统计物理学框架内，这些模型的性质不同的行为可以与物质相关联，这为计算统计物理学和液体理论中的既定概念提供了强有力的联系。在这个项目中，我们的目标是利用这一联系，并制定一个全面的理论描述相行为的非平衡聚集的自推进粒子结合两种互补的方法：大规模的数值模拟和分析建模的基础上的动态平均场理论。

We study the collective behavior of minimal models for artificial colloidal self-propelled particles. These "active" particles are envisioned for a range of applications, e.g., for the efficient delivery of drugs and the design of non-equilibrium materials with structures and functionalities that would be impossible to achieve in passive materials. The models are characterized by the combination of short-range interactions with directed motion, the sustenance of which requires steady work. Even though these systems are genuinely out-of-equilibrium, the observed large-scale collective behavior often resembles that of passive systems, which can tentatively be explained due to the local, autonomous character of the driving. Within a statistical physics framework, qualitatively different behavior of these models can be associated with phases of matter, which provides a powerful link to established concepts in computational statistical physics and the theory of liquids. In this project, we aim to exploit this link and to develop a comprehensive theoretical description for the phase behavior of non-equilibrium aggregation of self-propelled particles combining two complementary approaches: large-scale numerical simulations and analytical modeling based on a dynamical mean-field theory.