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Achieving designed texture and flows in bulk active nematics using optimal control theory
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Being intrinsically nonequilibrium, active materials can potentially perform functions that would be thermodynamically forbidden in passive materials. However, active systems have diverse local attractors that correspond to distinct dynamical states, many of which exhibit chaotic turbulent-like dynamics and thus cannot perform work or useful functions. Designing such a system to choose a specific dynamical state is a formidable challenge. Motivated by recent advances enabling opto-genetic control of experimental active materials, we describe an optimal control theory framework that identifies a spatiotemporal sequence of light-generated activity that drives an active nematic system toward a prescribed dynamical steady-state. Active nematics are unstable to spontaneous defect proliferation and chaotic streaming dynamics in the absence of control. We demonstrate that optimal control theory can compute activity fields that redirect the dynamics into a variety of alternative dynamical programs and functions. This includes dynamically reconfiguring between states, and selecting and stabilizing emergent behaviors that do not correspond to attractors, and are hence unstable in the uncontrolled system. Our results provide a roadmap to leverage optical control methods to rationally design structure, dynamics, and function in a wide variety of active materials.
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Cited by 1 Pith paper
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Tailoring interactions between active nematic defects with reinforcement learning
A reinforcement learning agent uses local activity fields to steer active nematic defects along designer trajectories such as overdamped springs, with only coarse, low-dimensional feedback.
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