Zhang Nan, Gao Feng, Wang Ride, Shen Zhonglei, Han Donghai, Cui Yuqing, Zhang Liuyang, Chang Chao, Qiu Cheng-Wei, Chen Xuefeng
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China.
Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing, 100071, P. R. China.
Adv Mater. 2025 Jan;37(1):e2411490. doi: 10.1002/adma.202411490. Epub 2024 Oct 27.
As a 2D metamaterial, metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. The current "one-by-one design" method is hindered by time-consuming, repeated testing within a confined space. However, intelligent design strategies for metasurfaces, limited by data-driven properties, have rarely been explored. To address this gap, a data iterative strategy based on deep learning, coupled with a global optimization network is proposed, to achieve the customized design of chiral metasurfaces. This methodology is applied to precisely identify different chiral molecules in a label-free manner. Fundamentally different from the traditional approach of collecting data purely through simulation, the proposed data generation strategy encompasses the entire design space, which is inaccessible by conventional methods. The dataset quality is significantly improved, with a 21-fold increase in the number of chiral structures exhibiting the desired circular dichroism (CD) response (>0.6). The method's efficacy is validated by a monolayer structure that is easily prepared, demonstrating advanced sensing abilities for enantiomer-specific analysis of bio-samples. These results demonstrate the superior capability of data-driven schemes in photonic design and the potential of chiral metasurface-based platforms for calibration-free biosensing applications. The proposed approach will accelerate the development of complex systems for rapid molecular detection, spectroscopic imaging, and other applications.
作为一种二维超材料，超表面为促进光与物质的相互作用提供了一条前所未有的途径。当前的“逐个设计”方法受到在有限空间内进行耗时、重复测试的阻碍。然而，受数据驱动特性限制的超表面智能设计策略很少被探索。为了填补这一空白，提出了一种基于深度学习的数据迭代策略，并结合全局优化网络，以实现手性超表面的定制设计。该方法被应用于以无标记方式精确识别不同的手性分子。与单纯通过模拟收集数据的传统方法根本不同，所提出的数据生成策略涵盖了整个设计空间，这是传统方法无法触及的。数据集质量显著提高，表现出所需圆二色性（CD）响应（>0.6）的手性结构数量增加了21倍。该方法的有效性通过易于制备的单层结构得到验证，展示了对生物样品进行对映体特异性分析的先进传感能力。这些结果证明了数据驱动方案在光子设计中的卓越能力以及基于手性超表面的平台在无校准生物传感应用中的潜力。所提出的方法将加速用于快速分子检测、光谱成像和其他应用的复杂系统的开发。
