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Item type:Publication, Access status: without files , Geometric approach to design of photonic crystals and metamaterials via optimal toric packingsDesign of photonic crystals having large band gaps above a prescribed band is a well-known physical problem with many applications. A connection to an interesting mathematical construction was pointed out some time ago: It had been conjectured that optimal structures for gaps between bands n and n + 1 correspond, in the case of transverse magnetic polarization, to rods located at the generators of centroidal Voronoi tessellation (CVT) and, in the case of transverse electric polarization, to the walls of this tessellation. We discover another mathematical recipe which produces even better solutions: optimal packing of disks in square and triangular tori. It provides solutions qualitatively different from CVT, sometimes increasing the resulting band gap size several times. We therefore introduce two classes of periodic structures with remarkable properties which may find applications in many other areas of modern solid state physics: arrays of particles located at the centers of optimally packed disks on tori and nets corresponding to the walls of their Voronoi tessellations.Publicationtype: Journal Article17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Geometric approach based on optimal toric packings for photonic crystals and metamaterials designWe introduce new classes of nanostructures—optimal toric packings of particles and their Voronoi tessellations—that exhibit remarkable properties for photonic and potentially for plasmonic/phononic crystals design. These structures connect the classical problem of optimal packings on tori with the development of optimized photonic architectures.Publicationtype: Conference Paper33 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Machine learning models for photonic crystals band diagram prediction and gap optimisation(2022-12); ; ; ; 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.Publicationtype: Journal Article84