Externally directing self-assembly with dynamic programming

This paper describes a methodology for establishing feedback control of self-assembly dynamics in order to reach a desired assembled state. The methodology consists of three sequential steps: 1) selection of metrics that characterize the aggregate state of the system, 2) application of machine learning to develop an empirical model of the aggregate state dynamics, and 3) application of dynamic programming to obtain a feedback control policy for reaching the desired assembled state. The strategy is illustrated for colloidal assembly and salt crystallization. In particular, the framework is used to develop feedback control policies for quickly producing perfect colloidal crystals and for producing salt crystals of a target average size. Simulation and experimental results are provided that demonstrate the application of these control policies to achieve the stated objectives.

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