Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders
Muammer Y. Yaman
Department of Chemistry, University of Washington, Seattle, WA, 98195 USA
Search for more papers by this authorSergei V. Kalinin
Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, 37996 USA
Search for more papers by this authorKathryn N. Guye
Department of Chemistry, University of Washington, Seattle, WA, 98195 USA
Search for more papers by this authorCorresponding Author
David S. Ginger
Department of Chemistry, University of Washington, Seattle, WA, 98195 USA
Physical Sciences Division, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354 USA
E-mail: [email protected]; [email protected]
Search for more papers by this authorCorresponding Author
Maxim Ziatdinov
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
E-mail: [email protected]; [email protected]
Search for more papers by this authorMuammer Y. Yaman
Department of Chemistry, University of Washington, Seattle, WA, 98195 USA
Search for more papers by this authorSergei V. Kalinin
Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, 37996 USA
Search for more papers by this authorKathryn N. Guye
Department of Chemistry, University of Washington, Seattle, WA, 98195 USA
Search for more papers by this authorCorresponding Author
David S. Ginger
Department of Chemistry, University of Washington, Seattle, WA, 98195 USA
Physical Sciences Division, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354 USA
E-mail: [email protected]; [email protected]
Search for more papers by this authorCorresponding Author
Maxim Ziatdinov
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
E-mail: [email protected]; [email protected]
Search for more papers by this authorAbstract
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.
Conflict of Interest
The authors declare no conflict of interest.
Open Research
Data Availability Statement
The data that support the findings of this study are openly available in https://github.com/ziatdinovmax/dualVAE at https://github.com/ziatdinovmax/dualVAE, reference number 1.
Supporting Information
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| smll202205893-sup-0001-SuppMat.pdf1.9 MB | Supporting Information |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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