The group led by professor Junsuk Rho taught an artificial neural network to recognize the correlation between the extinction spectra of electric and magnetic dipoles and core-shell nanoparticle designs that included material information and shell thicknesses. The AI system allowed greater freedom of design by categorizing types of materials and adding material types as a design factor, making it possible to design materials to meet specific optical requirements. An analysis of the metamaterials obtained through this design method showed that they exhibited optical properties that were identical to the properties that were input into the artificial neural network.
![Simultaneous inverse metamaterial design using data-driven approach. POSTECH.](https://www.photonics.com/images/Web/Articles/2019/7/19/REAS_POSTECH_Artificial_intelligence_AI_designs_metamaterials_used_in_the_invisibility_cloak_1.jpg)
The use of an artificial neural network significantly reduced the time needed to design photonic structures, the researchers said. Also, it allowed various designs of new metamaterials because the researchers were no longer limited by the need to develop designs intuitively, based on observations and trial-and-error.
![Simultaneous inverse metamaterial design using data-driven approach. POSTECH.](https://www.photonics.com/images/Web/Articles/2019/7/19/REAS_POSTECH_Artificial_intelligence_AI_designs_metamaterials_used_in_the_invisibility_cloak_2.jpg)
Regarding his team’s approach to metamaterials design, professor Rho said, “Our research was successful in bringing it to a higher degree of freedom of the design, but the new design still requires users to input certain problem settings in the beginning. It sometimes produced wrong designs and therefore made it impossible to produce desired metamaterials.
“So, I’d like to take our findings a step further by developing a complete design method of metamaterials utilizing AI. Also, I’d like to make innovative and practical metamaterials by training AI with reviews of the design constructed in consideration of final products.”
AI can learn designs of various metamaterials and the correlation between photonic structures and their optical properties. Using this training process, AI can provide a design approach to efficiently make a photonic structure with desired optical properties. Once trained, it can provide a desired design promptly and efficiently. AI-based approaches to metamaterials design have already been researched, but according to the POSTECH team, the previous studies have required material and structural parameters to be input beforehand and for photonic structures to be adjusted after the design is completed.
The research was published in ACS Applied Materials & Interfaces (https://pubs.acs.org/doi/10.1021/acsami.9b05857).