Control of Self-Assembly with Dynamic Programming

Abstract This conference paper updates a previously-reported methodology for establishing feedback control of self-assembly (Griffin et al. (2016b)). The methodology combines dimension reduction, supervised learning, and dynamic programming to obtain an optimal feedback control policy for reaching a desired assembled state. The strategy is further demonstrated, with both simulation and experimental results, for two applications: control of colloidal assembly (to produce perfect colloidal crystals) and control of crystallization from solution (to produce crystals of desired average size).

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