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Related Experiment Video

Updated: Jun 12, 2026

Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation
09:29

Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation

Published on: September 27, 2011

Physics-aware graph neural networks for optically interacting plasmonic nanoparticle assemblies.

Abolfazl A Davidi, Tavakol Pakizeh

    Optics Express
    |June 11, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces a faster graph neural network (GNN) for designing nanophotonics using hybrid datasets. This AI approach accelerates the development of optical devices with interacting nanoparticles.

    Area of Science:

    • Nanophotonics and nano-optics
    • Computational electromagnetics
    • Artificial intelligence in materials science

    Background:

    • Artificial intelligence (AI)-based surrogate models accelerate nanophotonics and nano-optics design.
    • High costs of generating large, high-fidelity training datasets limit AI model development.
    • Developing efficient AI frameworks is crucial for advancing nanophotonic device design.

    Purpose of the Study:

    • Introduce a drastically accelerated graph neural network (GNN) framework for nanophotonics design.
    • Employ multi-fidelity hybrid datasets combining analytical and numerical simulations.
    • Investigate optical responses of nanostructures with strongly interacting plasmonic nanoparticles.

    Main Methods:

    • Construct a physics-aware graph neural network (GNN) architecture.

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    Evaluating Plasmonic Transport in Current-carrying Silver Nanowires
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    Evaluating Plasmonic Transport in Current-carrying Silver Nanowires

    Published on: December 11, 2013

    Related Experiment Videos

    Last Updated: Jun 12, 2026

    Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation
    09:29

    Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation

    Published on: September 27, 2011

    Evaluating Plasmonic Transport in Current-carrying Silver Nanowires
    09:00

    Evaluating Plasmonic Transport in Current-carrying Silver Nanowires

    Published on: December 11, 2013

  • Utilize multi-fidelity hybrid datasets from coupled-dipole approximation (CDA) and high-accuracy numerical simulations.
  • Represent nanoparticles as nodes and their interactions as physics-aware edge features using a polarizability-augmented GNN.

    • Main Results:

    • Demonstrated efficiency and performance for nano-dimer and nano-trimer systems.
    • Highlighted the framework's capacity to integrate theoretical and semi-analytical models.
    • Showcased a multi-fidelity, physics-aware data strategy for AI in nanophotonics.

    Conclusions:

    • The proposed GNN framework significantly accelerates nanophotonic design.
    • The approach effectively integrates diverse data sources within a physics-aware architecture.
    • The framework is readily extendable to complex nanophotonic systems and intricate nanoparticle assemblies.