Optimal feedback control of batch self-assembly processes using dynamic programming. (April 2020) - Grover, Martha A.; Griffin, Daniel J.; Tang, Xun; Kim, Youngjo; Rousseau, Ronald W.
Highlights: Optimal control policies are calculated for two applications in crystallization. The policies can be learned from simulation data or experimental data. Desired crystallinity targets can be achieved robustly using feedback. Abstract: This paper reviews a previously-reported methodology for establishing feedback control of self-assembly. The methodology combines dimension reduction, supervised learning, and dynamic programming to obtain an optimal feedback control policy for reaching a desired assembled state. Sampled data are used in calculating the optimal feedback policy; this data can be generated using a predictive model (i.e. "simulated data") or using experimental data. The control strategy is 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).