Predictability of Localized Plasmonic Responses in Nanoparticle Assemblies
Predictability of Localized Plasmonic Responses in Nanoparticle Assemblies
复制标题
纳米颗粒组件中局域等离子体响应的可预测性
DOI：
10.1002/smll.202100181
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发表时间：
2021
期刊：
影响因子：
13.3
通讯作者：
Kalinin, Sergei V.
中科院分区：
文献类型：
--
作者：
Roccapriore, Kevin M.;Ziatdinov, Maxim;Cho, Shin Hum;Hachtel, Jordan A.;Kalinin, Sergei V.
设计具有理想光学性能的纳米结构是纳米光子学的关键任务。在这里，利用编码器-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。在该网络中，通过将观测到的几何形状编码为少量潜在变量，然后解码为等离子体谱，建立了局部粒子几何形状与局部谱之间的关系；在spec2imnetwork中，这种关系是相反的。令人惊讶的是，这些简化的描述允许基于固定成分和表面化学状态的几何形状的局部响应的高精度预测。分析潜在空间分布以及相应的解码和最接近（在潜在空间中）编码图像，可以深入了解纳米颗粒阵列中等离子体相互作用的生成机制。最终，这种方法为确定产生最接近期望光谱的配置创造了一条道路，为纳米等离子体结构的随机设计铺平了道路。
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 theim2specnetwork, 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 thespec2imnetwork, 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.