A deep learning approach to the forward prediction and inverse design of plasmonic metasurface structural color

Roberts, Nathan Bryn; Hedayati, Mehdi Keshavarz

doi:10.1063/5.0055733

Cited by 43 publications

(21 citation statements)

References 31 publications

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“…The weights in the inverse network were trained by minimizing the error between the real property input to the tandem network and the output property predicted by the tandem network. Other studies [120][121][122][123] have similarly reported that this strategy helps training convergence, despite the inverse network itself being multivalued, because the training losses are defined only by the property loss and not on the error between the predicted and actual design parameters. The tandem inverse architecture was also found to be effective for simultaneously predicting a combination of discrete design parameters (materials indexed by numbering) and continuous structural parameters (thicknesses) displaying a targeted optical spectrum.…”

Section: Inverse Networkmentioning

confidence: 99%

Machine learning-assisted design of material properties

Kadulkar,

Sherman,

Ganesan

et al. 2022

Preprint

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Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time-and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. Here, we review how to leverage machine learning to reduce dimensionality to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties.

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Section: Inverse Networkmentioning

confidence: 99%

Machine learning-assisted design of material properties

Kadulkar,

Sherman,

Ganesan

et al. 2022

Preprint

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“…One example is that of localized surface plasmon resonances (LSPRs) which occur when light is trapped between conductive, nanometer-sized features leading to selective resonant absorption and hybridized reflectance modes resulting in intensely saturated, colored metasurface films. Selective coloration can therefore be achieved by engineering unit cell dimensions appropriately [1][2][3] . In this work, we describe a model for inversely obtaining meta-unit geometries to achieve structural color produced 'at will' .…”

Section: Modified Variational Autoencoder For Inversely Predicting Pl...mentioning

confidence: 99%

Modified variational autoencoder for inversely predicting plasmonic nanofeatures for generating structural color

Pillai

Rai

Pal

2023

Sci Rep

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We apply a modified variational autoencoder (VAE) regressor for inversely retrieving the topological parameters of the building blocks of plasmonic composites for generating structural colors as per requirement. We demonstrate results of a comparison study between inverse models based on generative VAEs as well as conventional tandem networks that have been favored traditionally. We describe our strategy for improving the performance of our model by filtering the simulated dataset prior to training. The VAE- based inverse model links the electromagnetic response expressed as the structural color to the geometrical dimensions from the latent space using a multilayer perceptron regressor and shows better accuracy over a conventional tandem inverse model.

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“…In addition, there are multiple other works using deep learning to efficiently design structural colors in plasmonic and metamaterial structures. [185][186][187] Other practical metasurfaces applications designed by intelligent algorithms include light sails, [188,189] optical information storage (Figure 7D), [162] perfect absorbers, [190] biosensors, [191][192][193][194] and energy conversions. [195] In these metasurface designs, intelligent algorithms perform not only faster, but also more powerful than traditional methods.…”

Section: Intelligent Meta-applicationsmentioning

confidence: 99%

“…In addition, there are multiple other works using deep learning to efficiently design structural colors in plasmonic and metamaterial structures. [ 185–187 ]…”

Section: Design Metastructures With Ai Algorithmsmentioning

confidence: 99%

Metamaterials: From fundamental physics to intelligent design

Chen

Zhu

et al. 2022

Interdisciplinary Materials

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Metamaterials are artificial structures with the ability to efficiently control light‐field, attracting intensive attention in the past few decades. People have studied the working principles, design strategies, and fabrication methods of metamaterials, making this field cross and combine with many disciplines, including physics, material science, electronics, and chemistry. In recent years, with the rapid development of high‐efficiency and multifunctional metasurfaces, which are a two‐dimensional version of metamaterials, great efforts have been made to push this material to practical applications. In particular, the introduction of artificial intelligent (AI) algorithms enables metamaterials‐based photonic devices that exhibit excellent performances and intelligent functionalities. In this review, we first introduce the basic concepts, working principles, design methods, and applications of metamaterials, and then focus on the rapidly developing metamaterials research combined with AI algorithms. Finally, we conclude this review with personal perspectives on the current problems and future directions of metamaterials research and developments.

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A deep learning approach to the forward prediction and inverse design of plasmonic metasurface structural color

Abstract: Mehdi (2021) 'A deep learning approach to the forward prediction and inverse design of plasmonic metasurface structural color.', Applied physics letters., 119 (6).

Cited by 43 publications

References 31 publications

Machine learning-assisted design of material properties

Machine learning-assisted design of material properties

Modified variational autoencoder for inversely predicting plasmonic nanofeatures for generating structural color

Metamaterials: From fundamental physics to intelligent design

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