11:00 AM - EQ04.04.02
Prediction of Localized Plasmon Resonances in Complex Nanoparticle Assemblies Using Autoencoder Networks
Kevin Roccapriore1,Maxim A. Ziatdinov1,Shin-Hum Cho2,Delia Milliron3,Jordan Hachtel1,Sergei Kalinin1
Oak Ridge National Laboratory1,Samsung Semiconductor R&D2,The University of Texas at Austin3
Show Abstract
Designing nanostructures with desired optical properties is critical to the field of nanophotonics. Traditionally, numerical methods and theoretical predictions are used to inform on design parameters of nanophotonic elements. On the other hand, having knowledge of the structure-property relationships of a given system provides another data-driven approach to design nanostructures. To pave the way towards stochastic design of nanoplasmonic structures, we establish the correlative relationship between local nanoparticle geometries and their plasmonic responses. By using an encoder-decoder neural networks within the framework of PyTorch, we demonstrate the predictions of spectra from local geometries in the im2spec network, as well as the predictions of local geometry from spectral inputs in the spec2im network [1].
By utilizing scanning electron transmission electron microscopy (STEM), we acquire an electron energy loss (EEL) spectrum image and its associated acquired high angle annular dark field (HAADF) image of a self-assembled monolayer of fluorine and tin doped indium oxide nanocrystal arrays [2]. While the particles mainly undergo self-assembly, many defects and irregularities are observed within and nearby the arrays – for example, missing particles, small clusters of particles, etc. With the knowledge that the plasmonic behavior at a position in space depends primarily on local geometry at that position, subimage-spectrum pairs are generated at each spatial pixel that are used to teach the network the correlative relationship between them. The network encodes the images (spectra) to a low-dimensional feature space consisting of a small number of latent variables, then decodes the latent variables back into spectra (images). The reduced descriptions contained in the so-called latent space can yield a surprising insight into the generative mechanisms of complicated plasmonic responses in nanoparticle arrays [3].
Equipped with the knowledge of correlative structure property relationships gained by the autoencoder networks, we predict the plasmonic response given an unseen nanoparticle geometry. Conversely, we predict nanoparticle geometry given a plasmonic spectrum. This method ultimately enhances the design capabilities for plasmonic systems, and may even permit a route to solving the inverse design challenge in nanophotonics.
[1] S. V. Kalinin et al., Adv. Optical Mater. 2021, 2001808.
[2] S. H. Cho et al., J., Chem. Mater. 2019 31, 2661.
[3] K. M. Roccapriore et al., Small 2021 17, 2100181.
This effort (ML and STEM) is based upon work supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (K.M.R., S.V.K.) and was performed and partially supported (J.A.H., M.Z.) at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility. S.H.C acknowledges (NSF, CHE-19052631609656, CBET-1704634, NASCENT, an NSF ERC EEC-1160494, and CDCM, an NSF MRSEC DMR-1720595), the Welch Foundation (F-1848), and the Fulbright Program (IIE-15151071).