Zhang Jianli, Yang Junyan, Zhang Yuanxing, Bevan Michael A
Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Sci Adv. 2020 Nov 25;6(48). doi: 10.1126/sciadv.abd6716. Print 2020 Nov.
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 = 10 particles with mean process times scaling as 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，该方法利用变形能量景观和基于强化学习的策略来消除晶界并生成圆形胶体晶体。我们在光学显微镜和交流电场中胶体颗粒的计算机模拟实验中展示了这种方法。首先，我们发现了可调谐能量景观形状和取向如何增强晶界运动和晶体形态弛豫。接下来，利用强化学习开发一种优化的控制策略，以驱动变形能量景观，从而比自然弛豫时间快几个数量级地产生无缺陷晶体。变形能量景观通过各向异性应力机械地实现快速晶体修复，以控制缺陷和形状弛豫而无需熔化。该方法可扩展至至少(N = 10^6)个粒子，平均处理时间按(N^{1/3})缩放。通过控制并行局部能量景观（例如周期性景观）以生成大规模全局无缺陷分层结构，进一步的可扩展性是可能的。
