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).

中文翻译：

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使用动态规划的批量自组装过程的最优反馈控制

摘要 本文回顾了先前报道的建立自组装反馈控制的方法。该方法结合了降维、监督学习和动态规划，以获得用于达到所需组装状态的最佳反馈控制策略。采样数据用于计算最优反馈策略；该数据可以使用预测模型（即“模拟数据”）或使用实验数据生成。控制策略通过模拟和实验结果证明，适用于两种应用：控制胶体组装（以产生完美的胶体晶体）和控制溶液结晶（以产生所需平均尺寸的晶体）。