About this Abstract |
Meeting |
Materials Science & Technology 2020
|
Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
Presentation Title |
Feature Characterization of Electron Backscatter Patterns from Rotating Lattice Single Crystals Using Machine Learning |
Author(s) |
Evan J. Musterman, Joshua Agar, Volkmar Dierolf, Himanshu Jain |
On-Site Speaker (Planned) |
Evan J. Musterman |
Abstract Scope |
Crystallographic information with high spatial resolution can be acquired in the scanning electron microscope through electron backscatter diffraction (EBSD) techniques. Diffracted electrons create Kikuchi bands across backscatter patterns which are fit to a particular crystal phase and orientation. These patterns, acquired on a pixel-by-pixel basis, create large multidimensional datasets which are generally reduced to a few parameters with conventional EBSD analysis. Using Sb2S3 rotating lattice single (RLS) crystals in glass for their novel crystallography, we demonstrate a novel example of unsupervised machine and deep learning, such as convolutional neural networks, to identify and visualize latent features in the EBSD datasets beyond the conventional analysis. These models exhibit the ability to distinguish crystal from glass and identify crystal rotation. A comparison of this analysis is made for an RLS crystal vs. a polycrystalline sample. |