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Meeting Materials Science & Technology 2020
Symposium AI for Big Data Problems in Imaging, Modeling and Synthesis
Presentation Title Image Characterization of Self-assembled Photonic Crystals and Glasses Using Machine Learning
Author(s) Bo Lei, Yen Häntsch, Gerold A. Schneider, Kaline P. Furlan, Elizabeth Holm
On-Site Speaker (Planned) Bo Lei
Abstract Scope 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.


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Directing Matter In-situ via Deep Learning
Enabling Data-driven Discovery of Chemistry-function Relationships via Automated Packing Motif Labeling
Image Characterization of Self-assembled Photonic Crystals and Glasses Using Machine Learning
Instance Segmentation for Autonomous Detection of Individual Powder Particles and Satellites in an Additive Manufacturing Feedstock Powder
Inverse Design of Porous Structures by Deep Learning and TPU-based Computing
Polymer Informatics—Current Status and Critical Next Steps
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