论文标题
使用人工神经网络分析的淬灭薄膜膜中的拓扑缺陷变形
Topological defect coarsening in quenched smectic-C films analyzed using artificial neural networks
论文作者
论文摘要
机械地淬灭近晶液晶体的薄膜导致导演场中成千上万的拓扑缺陷的密集阵列形成。随后使用高速的，偏光的轻型视频显微镜捕获了相反符号缺陷的膜质地的快速变形。使用对象检测卷积神经网络来确定缺陷位置，并定制了二进制分类网络，以评估缺陷周围的刷子方向动力学，以确定其拓扑符号。在淬火之后的早期，空间分辨率的固有限制导致降低了缺陷和与预期行为的偏差。在中间到后期，观察到的歼灭动力学量表与$ 2 $ d xy模型的理论预测和模拟一致。
Mechanically quenching a thin film of smectic-C liquid crystal results in the formation of a dense array of thousands of topological defects in the director field. The subsequent rapid coarsening of the film texture by the mutual annihilation of defects of opposite sign has been captured using high-speed, polarized light video microscopy. The temporal evolution of the texture has been characterized using an object-detection convolutional neural network to determine the defect locations, and a binary classification network customized to evaluate the brush orientation dynamics around the defects in order to determine their topological signs. At early times following the quench, inherent limits on the spatial resolution result in undercounting of the defects and deviations from expected behavior. At intermediate to late times, the observed annihilation dynamics scale in agreement with theoretical predictions and simulations of the $2$D XY model.
