We report a feedback control method to remove grain boundaries and produce circular shaped colloidal crystals using morphing energy landscapes and reinforcement learning–based policies. We demonstrate this approach in optical microscopy and computer simulation experiments for colloidal particles in ac electric fields. First, we discover how tunable energy landscape shapes and orientations enhance grain boundary motion and crystal morphology relaxation. Next, reinforcement learning is used to develop an optimized control policy to actuate morphing energy landscapes to produce defect-free crystals orders of magnitude faster than natural relaxation times. Morphing energy landscapes mechanistically enable rapid crystal repair via anisotropic stresses to control defect and shape relaxation without melting. This method is scalable for up to at least N = 10³ particles with mean process times scaling as N^0.5. Further scalability is possible by controlling parallel local energy landscapes (e.g., periodic landscapes) to generate large-scale global defect-free hierarchical structures.

我们报告了一种反馈控制方法，该方法使用变形能态和基于强化学习的策略来消除晶界并产生圆形胶体晶体。我们在光学显微镜和计算机模拟实验中证明了这种方法，用于交流电场中的胶体颗粒。首先，我们发现可调谐能量景观的形状和方向如何增强晶界运动和晶体形态弛豫。接下来，通过强化学习来开发优化的控制策略，以激活变形能量态势，以产生比自然弛豫时间快几个数量级的无缺陷晶体。机械变形能量能通过各向异性应力以机械方式实现快速晶体修复，从而控制缺陷和形状松弛而不熔化。此方法至少可扩展到N = 10 ^3个粒子，平均处理时间缩放为N ^0.5。通过控制并行的局部能量分布（例如周期性分布）以生成大规模的全局无缺陷分层结构，可以实现进一步的可伸缩性。