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The process for designing metamaterials could be improved by using data-driven approaches based on deep learning, according to researchers at Pohang University of Science and Technology (POSTECH). The researchers used a deep-learning-assisted inverse design method to enable structural parameters and material for metamaterials to be designed simultaneously and with a greater degree of freedom.
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…READ MORE