Collaborative Research: DMREF: Adaptive and Responsive Magnetic Swarms (ARMS)

Non-technical description: The goal of this project is to build and steer swarms of micron-scale magnetic colloidal particles that come together and move cooperatively through complex environments, much like schools of fish, flocks of birds, or swarms of insects. These swarms are activated by a time-varying magnetic source (for example, an electromagnet or a moving permanent magnet) which functions as an external remote controller. The magnetic controller can direct swarms to propel through fluids, maneuver over surfaces and around obstacles, detect and respond to changes in their surroundings, and carry passive cargo. This project aims to advance the field of magnetic swarms by integrating large computer simulations, theoretical modeling, and experimental approaches within a cohesive framework. Mastering life-like swarm behavior could enable miniature ARMS robots that deliver medicine inside the body, inspect subsurface pipelines, or remove contaminants from water supplies. By opening new frontiers in materials science and programmable matter, this project advances the nation’s health, prosperity, and security while strengthening technological leadership. This project will also provide K-12, undergraduate, and graduate students with interdisciplinary training in computational and experimental techniques for materials science, physics, and engineering to develop our domestic workforce, improve public scientific literacy, and stimulate engagement with science and technology. Technical description: While magnetic swarms capable of dynamic reorganization have been demonstrated, a systematic approach to designing swarms with increasingly sophisticated functions in porous environments and unbounded 3D fluids remains a challenge. Large-scale simulations will capture the coupled magnetic, hydrodynamic, and contact interactions that drive collective motion across multiple length and time scales. Analytical theory will translate these data into design rules, while inverse-design algorithms will search efficiently for particle shapes, magnetic moments, and field protocols that enable adaptive aggregation to move through complex structures. Lithographically fabricated and chemically synthesized particles will test these predictions; high-speed imaging, particle tracking, and force mapping experiments will measure swarm structure, flow fields, and cargo transport efficiency. By combining computational analysis with experimental methods, swarm functionalities for advanced applications, such as adaptive organization, precise navigation, and targeted cargo transport in complex environments will be expanded. These advancements will create a foundation for future applications of colloidal swarms in sensing and delivery, turning theoretical insights into practical outcomes. More broadly, the proposed methods to accelerate swarm design will benefit other active material systems where flows of energy, matter, and information animate material structures to enable life-like capabilities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.