|Publisher DOI:||10.1016/j.photonics.2022.101076||Title:||Machine learning models for photonic crystals band diagram prediction and gap optimisation||Language:||English||Authors:||Nikulin, A.
Petrov, Alexander Yu.
|Keywords:||Machine learning; Neural networks; Photonic bandgap materials; Photonic crystals||Issue Date:||Dec-2022||Source:||Photonics and Nanostructures - Fundamentals and Applications 52: 101076 (2022-12)||Abstract (english):||
Data-driven methods of machine learning (ML) have attracted a lot of interest in various fields of physics. Inverse design and optimisation of structured optical metamaterials such as photonic crystals, metasurfaces, and other nanostructured components seem to benefit a lot from this approach in the nearest future. Here we develop several approaches to use ML methods to predict and optimise properties of photonic crystals (e.g. size of bandgaps) effectively. We use a dataset of 2D photonic crystals produced recently in [T.Christinsen et al., Nanophotonics 9, 4183 (2020)]. For improving performance of predictive models, we apply symmetry-aware augmentations and hybrid ML-solver approaches. As a result, considerable improvement in prediction accuracy could be achieved as compared to baseline models. For generative models, we apply variational autoencoders (VAEs) combined with predictor architecture, inspired by related works in chemical design realm. By using latent space optimisation, we achieve good results in the task of increasing bandgaps of photonic structures. The approach seems to be very promising and can be extended to 3D geometries.
|URI:||http://hdl.handle.net/11420/13829||ISSN:||1569-4410||Journal:||Photonics and nanostructures||Institute:||Optische und Elektronische Materialien E-12||Document Type:||Article||Project:||SFB 986: Teilprojekt C01 - Multiskalige photonische Materialien mit anpassbarer Absorption und thermischer Emission
AI for photonics
SFB 986: Teilprojekt C02 - Multiskalige photonische Materialien mit anpassbarer radialer Anisotropie
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