论文标题
通过双重变异自动编码器学习和预测等离子纳米颗粒组件的光子响应
Learning and predicting photonic responses of plasmonic nanoparticle assemblies via dual variational autoencoders
论文作者
论文摘要
我们证明了机器学习的应用，以通过双重变异自动编码器（Dual-VAE）从高光谱图像数据中快速，准确地提取等离子体颗粒的几何形状。在这种方法中，该信息分别在两个VAE的潜在空间之间共享，该潜在空间分别作用于粒子形状数据和光谱数据，但在形状 - 光谱对上执行了常见的编码。 We show that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that we can use hyperspectral darkfield microscopy to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58).通过共享编码建立结构 - 特性关系的这种方法是普遍的，应该在分子和纳米材料系统的成像中应用更广泛的材料科学和物理问题。
We demonstrate the application of machine learning for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. We show that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that we can use hyperspectral darkfield microscopy to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.
