School of Physics, State Key Lab for Mesoscopic Physics, Academy for Advanced Interdisciplinary Studies, Nano-optoelectronics Frontier Center of Ministry of Education, Peking University, Beijing 100871, China.
Collaborative Innovation Center of Quantum Matter, Beijing 100871, China.
Phys Rev Lett. 2019 Nov 22;123(21):213902. doi: 10.1103/PhysRevLett.123.213902.
Optical chirality occurs when materials interact differently with light in a specific circular polarization state. Chiroptical phenomena inspire wide interdisciplinary investigations, which require advanced designs to reach strong chirality for practical applications. The development of artificial intelligence provides a new vision for the manipulation of light-matter interaction beyond the theoretical interpretation. Here, we report a self-consistent framework named the Bayesian optimization and convolutional neural network that combines Bayesian optimization and deep convolutional neural network algorithms to calculate and optimize optical properties of metallic nanostructures. Both electric-field distributions at the near field and reflection spectra at the far field are calculated and self-learned to suggest better structure designs and provide possible explanations for the origin of the optimized properties, which enables wide applications for future nanostructure analysis and design.
当材料以特定的圆偏振态与光相互作用时，就会发生光学手性。手性光学现象激发了广泛的跨学科研究，这需要先进的设计来达到实际应用的强手性。人工智能的发展为超越理论解释的光物质相互作用的操纵提供了新的视角。在这里，我们报告了一个名为贝叶斯优化和卷积神经网络的自洽框架，它结合了贝叶斯优化和深度卷积神经网络算法来计算和优化金属纳米结构的光学性质。近场的电场分布和远场的反射光谱都被计算和自我学习，以提出更好的结构设计，并为优化性质的起源提供可能的解释，这为未来的纳米结构分析和设计提供了广泛的应用。
