|About this Abstract
||Materials Science & Technology 2020
||AI for Big Data Problems in Imaging, Modeling and Synthesis
||Image Characterization of Self-assembled Photonic Crystals and Glasses Using Machine Learning
||Bo Lei, Yen Häntsch, Gerold A. Schneider, Kaline P. Furlan, Elizabeth Holm
|On-Site Speaker (Planned)
Photonic glasses fabricated by self-assembly of polystyrene particles demonstrate unique optical properties known as structural colors. The reflectance properties of such materials are governed by the structural parameters, the degree of order of the particles, and the material refractive index. Synchrotron X-ray tomography can be used for high-resolution structural characterization, but it is costly and time-consuming, thereby not suitable for fast characterization. Here, we propose that image analysis of SEM micrographs can be a very efficient approach for structural characterization. With the help of machine learning methods, we can achieve great accuracy in the classification of materials with different levels of disorder. We also show that it is possible to quantify the local structure and link it to the optical properties using image segmentation and machine learning.