Project description
Learning more about exotic light-scattering materials helps us to harness them
Like a bunch of pinballs reverberating around a pinball machine and bouncing off everything including each other, photons entering disordered materials are scattered several times before leaving the materials in random directions. The "dance" can continue, with the random rays interfering once again with each other. This amazing light show, if harnessed, can be used to develop innovative photonics devices. Until now, characterisation enabling the rational prediction of the properties of disordered materials and their subsequent design was an important roadblock. The EU-funded MALDIP project plans to apply machine-learning techniques and numerical simulations to get to the bottom of things.
Objective
The field of disordered photonics has increased its importance immensely over past decades as it finds widespread application in several fields from biomedical imaging, to solar energy harvesting, paint, pigments, food and cosmetic industry. However, the current development of highly scattering materials is often hindered by lack of ways to quantitatively predict and model their structural morphology and photonic properties. This action aims to characterize disordered photonic structures made of organic materials by analyzing their 3D structures using Gaussian Processes (GP) based machine learning techniques in conjunction with numerical optical simulations. The inherent randomness in the 3D arrangement of disordered photonics, makes them both intuitively and theoretically ideal to be modeled with GP. The novelty of this action consist of using state-of-the art GP method not only analyze 3D structures, but also to reconstruct them from lower dimensional data, like 2D images and spectroscopic data. Moreover by using the quantitative GP descriptors, we are able both generate input models for numerical simulations and using the feedback iteratively update those models to optimize them for high scattering. We expect that the complementary expertise in characterization and computational methods of the Host and the Researcher will produce not only invaluable insights, but also practical tools to characterize, quantify and exhaustively model and optimize complex photonic structures.
Fields of science
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
CB2 1TN Cambridge
United Kingdom