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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...
We propose a Markov decision based dynamic programming method to manipulate the self-assembly of a quadrupole colloidal system for grain-boundary-free two-dimensional crystals. To construct the optimal control policy, we developed a Markov chain model, based on information extracted from a Langevin dynamics simulation model, which originated from a more complicated Brownian dynamics model. An infinite-horizon...
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