ArticleACS applied materials & interfaces2024
Toward Machine-Learning-Accelerated Design of All-Dielectric Magnetophotonic Nanostructures.
Article in ACS applied materials & interfaces, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Sustainable Magnetochromic Flexible Composites for Advanced Optical Sensing and Smart Applications.ACS applied engineering materials · 2025Article
- Magnetic-Plasmonic Core-Shell Nanoparticles: Properties, Synthesis and Applications for Cancer Detection and Treatment.Nanomaterials (Basel, Switzerland) · 2025Review
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Authors and funding
5 authors.
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Abstract
All-dielectric magnetophotonic nanostructures are promising for integrated nanophotonic devices with high resolution and sensitivity, but their design requires computationally demanding electromagnetic simulations evaluated through trial and error. In this paper, we propose a machine-learning approach to accelerate the design of these nanostructures. Using a data set of 12 170 samples containing four geometric parameters of the nanostructure and the incidence wavelength, trained neural network and polynomial regression algorithms were capable of predicting the amplitude of the transverse magneto-optical Kerr effect (TMOKE) within a time frame of 10
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