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Magnetic particles confined in microchannels can be actuated to perform translation motion using a rotating magnetic field, but their actuation in such a situation is not yet well understood. Here, the actuation of a ferromagnetic particle confined in square microchannels is studied using immersed-boundary lattice Boltzmann simulations. In wide channels, when a sphere is positioned close to a side wall but away from channel corners, it experiences a modest hydrodynamic actuation force parallel to the channel walls. This force decreases as the sphere is shifted toward the bottom wall but the opposite trend is found when the channel is narrow. When the sphere is positioned midway between the top and bottom channel walls, the actuation force decreases as the channel width decreases and can reverse its direction. These phenomena are elucidated by studying the flow and pressure fields in the channel-particle system and by analyzing the viscous and pressure components of the hydrodynamic force acting on different parts of the sphere. 

Abstract Automated manipulation of small particles using external (e.g., magnetic, electric and acoustic) fields has been an emerging technique widely used in different areas. The manipulation typically necessitates a reduced‐order physical model characterizing the field‐driven motion of particles in a complex environment. Such models are available only for highly idealized settings but are absent for a general scenario of particle manipulation typically involving complex nonlinear processes, which has limited its application. In this work, the authors present a data‐driven architecture for controlling particles in microfluidics based on hydrodynamic manipulation. The architecture replaces the difficult‐to‐derive model by a generally trainable artificial neural network to describe the kinematics of particles, and subsequently identifies the optimal operations to manipulate particles. The authors successfully demonstrate a diverse set of particle manipulations in a numerically emulated microfluidic chamber, including targeted assembly of particles and subsequent navigation of the assembled cluster, simultaneous path planning for multiple particles, and steering one particle through obstacles. The approach achieves both spatial and temporal controllability of high precision for these settings. This achievement revolutionizes automated particle manipulation, showing the potential of data‐driven approaches and machine learning in improving microfluidic technologies for enhanced flexibility and intelligence.