Journal
SMALL
Volume 17, Issue 21, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/smll.202100181
Keywords
electron energy loss spectroscopy; machine learning; nanoparticle arrays; nanophotonics; plasmonics; scanning transmission electron microscopy
Categories
Funding
- U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division
- Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility
- NSF [CHE-19052631609656, CBET-1704634]
- NSF ERC [EEC-1160494]
- NSF MRSEC [DMR-1720595]
- Welch Foundation [F-1848]
- Fulbright Program [IIE-15151071]
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The correlation between local nanoparticle geometries and their plasmonic responses is established using encoder-decoder neural networks in this study. The simplified descriptions allow high-accuracy predictions of local responses based on geometries, paving the way for stochastic design of nanoplasmonic structures. This approach creates a path towards determining configurations that yield the spectrum closest to the desired one.
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 the im2spec network, 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 the spec2im network, 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.
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