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Plasmonic materials have the potential to control the light at sub-wavelength scales such as to reach extreme light localization, deep subwavelength resolution and enhanced light emission. This property arises from the coupling of light with free electrons at the metal-dielectric interface.  Plasmonic materials have found applications in bioimaging biosensing, integrated photonics, photovoltaics with higher efficiency, flat metalenses and metamaterials exhibiting exotic properties, such as negative refractive index and slow light propagation.  Finding the ideal material with advantages in device performance, design flexibility, fabrication, integration, and tunability.

Accelerating plasmonics R&D with machine learning

Machine learning algorithms in combination with novel experimental data can accelerate the process of materials design and discovery for plasmonic materials. Using machine learning algorithms on experimental data we can discover new plasmonic materials, get ‘materials recipes’ for synthesizing plasmonic materials, and build tools to automatically analyze new measurements to retrieve insights. This could help design new plasmonic materials for  bioimaging, biosensing, integrated photonics, photovoltaics, and other applications.

See what’s been done with machine learning for plasmonics:

  1. Ballard, Zachary S., et al. "Computational sensing using low-cost and mobile plasmonic readers designed by machine learning." ACS nano 11.2 (2017): 2266-2274.
  2. Razi, Mani, et al. "Optimization of large-scale Vogel spiral arrays of plasmonic nanoparticles." Plasmonics (2018): 1-9.

Baxter, J., et al. "Machine Learning Applications in Plasmonics." 2018 Photonics North (PN). IEEE, 2018.

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CASE STUDY | Combinatorial Lab Accelerates Discovery with MZ

Bar-Ilan conducts hundreds of experiments to identify new photovoltaic compositions. Leveraging the MaterialsZone platform, researchers experienced an 85% cost reduction during innovation cycle.

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