Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
Small. 2021 May;17(21):e2100181. doi: 10.1002/smll.202100181. Epub 2021 Apr 10.
Design of nanoscale structures with desired optical properties is a key task for nanophotonics. Here, the correlative relationship between local nanoparticle geometries and their plasmonic responses is established using encoder-decoder neural networks. In the im2spec network, the relationship between local particle geometries and local spectra is established via encoding the observed geometries to a small number of latent variables and subsequently decoding into plasmonic spectra; in the spec2im network, the relationship is reversed. Surprisingly, these reduced descriptions allow high-veracity predictions of local responses based on geometries for fixed compositions and surface chemical states. Analysis of the latent space distributions and the corresponding decoded and closest (in latent space) encoded images yields insight into the generative mechanisms of plasmonic interactions in the nanoparticle arrays. Ultimately, this approach creates a path toward determining configurations that yield the spectrum closest to the desired one, paving the way for stochastic design of nanoplasmonic structures.
设计具有所需光学特性的纳米结构是纳米光子学的一项关键任务。在这里，使用编解码器神经网络建立了局部纳米粒子几何形状与其等离子体响应之间的相关关系。在 im2spec 网络中，通过将观察到的几何形状编码成少数几个潜在变量，并随后将其解码为等离子体光谱，建立了局部粒子几何形状与局部光谱之间的关系；在 spec2im 网络中，关系是相反的。令人惊讶的是，这些简化的描述允许根据固定组成和表面化学状态的几何形状，对局部响应进行高精度预测。对潜在空间分布以及相应解码和最近（潜在空间中）编码图像的分析提供了对纳米粒子阵列中等离子体相互作用产生机制的深入了解。最终，这种方法为确定产生最接近所需光谱的配置开辟了道路，为纳米等离子体结构的随机设计铺平了道路。
