MALDIP · Machine Learning in Disordered Photonics

Machine Learning in Disordered Photonics

Creating highly scattering photonic materials from renewable and sustainable materials is outstanding scientific challenge. Structural colours, where the photonic response is based on nanoscopic structural features is a promising research field, compared to conventional pigment based colors, due to non-fadability of the material as long as the nanoscopic structures persist. In particular creating of structural white materials is of great importance since in the current commercial white paints the scattering components are often made from inorganic materials such as titanium dioxide TiO2 (refractive index n=2.6), which raise a number of safety and environmental concerns. Therefore discovering alternatives ways to produce safe and sustainable colored materials is of great importance and social impact, that has many important applications not only for paints and food additives but to medical imaging, solar cell efficiency etc. In the field of structural colors, disordered photonics has grown increasing interest due to biological examples (such as the white beetles) where highly efficient scattering materials are achieved using low refractive index (RI) materials such as chitin and cellulose(n ~1.55$). The scattering strength of such random material depend not only on their refractive index, on the geometry and spatial distribution of its components. While the value of the refractive index is easy to obtain, the precise knowledge of the morphology of the sample in terms of size and special arrangement of the scattering elements is very challenging to determine and quantify. The aims of this project were to 3D characterize and quantitatively model various disordered structures and combined with optical simulations understand how different structural features impact the optical properties to establish structure-property relationships.

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