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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.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerate TEM and Tomography Imaging by Deep-learning Enabled Compressive Sensing and Information Inpainting in High-dimensional Manifold
Assessment of the Ability of Laboratory Accelerated Corrosion Tests to Accurately Predict On-road Corrosion of 6xxx Al Alloys
Automated Optical Microscopy for Rapid Defect Screening
Computer Vision and Machine Learning for Microstructural Image Data
Developing Granular Dielectrics Based on Reconstructed Micro-CT Images
FAIR Digital Object Framework and High Throughput Experiment
Feature Characterization of Electron Backscatter Patterns from Rotating Lattice Single Crystals Using Machine Learning
Identifying Crack Initiation Sites with CNNs
Incorporating Materials Physics into Imaging Algorithms for Microscope Image Interpretation
Introductory Comments: Materials Informatics for Images and Multi-dimensional Datasets
Keyhole Porosity Threshold in Laser Melting Revealed by High-Speed X-ray Imaging
Microstructure Representation for Physically Meaningful Descriptors
Neural Networks and Community Driven Software for Scanning Transmission Electron Microscopy
Towards Smart Categorization of Growth Morphology by Machine Learning

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