Xia Xuanjie, Ni Miao, Wang Mengchen, Wang Bin, Liu Dong, Lu Yuan
Department of Chemical Engineering, Tsinghua University, Beijing 100084, China.
State Key Laboratory of Green Biomanufacturing, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China.
ACS Nano. 2025 Apr 8;19(13):12883-12894. doi: 10.1021/acsnano.4c16347. Epub 2025 Mar 26.
Mimicking the swarm behaviors in nature, the microswarm has shown dynamic transformations and flexible assemblies in complex physiological environments, garnering increasing attention for its potential medical applications. However, because of the complexity of swarm behaviors and the corresponding influencing factors, achieving controllability, stability, and diversity of an artificial microswarm remains challenging. Here, a physically assisted artificial intelligence analysis framework was employed to predict the multimode swarm behaviors of a magnetic microswarm. By modulating 12 different parameters of a programmable magnetic field, we obtained various swarm patterns, including liquid, rod, network, ribbon, flocculence, and vortex. A physical model was developed to simulate the programmable 3D magnetic field and the corresponding collective behaviors. Explainable artificial intelligence analysis uncovered the relationship between control parameters and magnetic swarm patterns, achieving a prediction accuracy of 83.87% for pattern classification. Our stability analysis revealed that rod and vortex patterns exhibited higher stability, making them ideal for precise manipulation tasks. Leveraging this framework, we demonstrated environmentally adaptive swarm navigation through complex channels and swarm hunting of specific targets. This study could not only advance the understanding of microswarm control but also provide a strategy for targeted delivery and micromanipulation in potential clinical applications.
模仿自然界中的群体行为，微型群体在复杂的生理环境中展现出动态转变和灵活组装，因其潜在的医学应用而受到越来越多的关注。然而，由于群体行为的复杂性以及相应的影响因素，实现人工微型群体的可控性、稳定性和多样性仍然具有挑战性。在此，采用了一种物理辅助的人工智能分析框架来预测磁性微型群体的多模式群体行为。通过调制可编程磁场的12个不同参数，我们获得了各种群体模式，包括液体、棒状、网络、带状、絮凝和涡旋。开发了一个物理模型来模拟可编程的三维磁场和相应的集体行为。可解释人工智能分析揭示了控制参数与磁性群体模式之间的关系，模式分类的预测准确率达到83.87%。我们的稳定性分析表明，棒状和涡旋模式表现出更高的稳定性，使其成为精确操纵任务的理想选择。利用这个框架，我们展示了通过复杂通道的环境适应性群体导航以及对特定目标的群体追捕。这项研究不仅可以推进对微型群体控制的理解，还可以为潜在临床应用中的靶向递送和显微操作提供一种策略。
