Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders |
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Authors: | Muammer Y Yaman Sergei V Kalinin Kathryn N Guye David S Ginger Maxim Ziatdinov |
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Affiliation: | 1. Department of Chemistry, University of Washington, Seattle, WA, 98195 USA;2. Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, 37996 USA;3. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USA |
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Abstract: | The application of machine learning is demonstrated 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. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used 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. |
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Keywords: | darkfield scattering spectra machine learning plasmonic gold particles scanning electron microscopy structure-property prediction variational autoencoder |
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