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Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Presentation Title 4D STEM Data Acquisition, Analytics and Functional Material Property Extraction
Author(s) Debangshu Mukherjee, Suhas Somnath, Alex Belianinov, Stephen Jesse, Raymond R. Unocic
On-Site Speaker (Planned) Raymond R. Unocic
Abstract Scope Aberration corrected scanning transmission electron microscopy (STEM) is a powerful characterization tool that allows for the structural and chemical analysis of materials at length scales down to the atomic scale using high spatial resolution imaging, electron diffraction and spectroscopy. Recent advances in high speed electron detectors has opened new opportunities to explore materials functionality using an approach termed 4D STEM. Here, a sub--sized electron probe is rastered through the material of interest and the scattered electrons, containing information rich, 2D diffraction patterns acquired pixel-by-pixel at every electron probe position, are collected by the detector. The 4D STEM data sets (e.g. probe position (x,y) and diffraction pattern (x’,y’), are inherently large which necessitates a big-data analytics approach to process then analyze the data to obtain meaningful information. Here we will discuss our data analytics approach with relevant examples for strain mapping of catalyst nanoparticles and electron ptychography of 2D materials.
Proceedings Inclusion? Definite: At-meeting proceedings

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

3D Nanoprinting: An Integrated Approach of Experiments, Computer-aided Design and Simulations
4D STEM Data Acquisition, Analytics and Functional Material Property Extraction
A Machine Learning Approach to Independent Component Analysis for Nuclear Magnetic Resonance Spectra
Adversarial Networks for Digital Microstructure Generation
Application of a Statistical Analysis Technique for Characterizing the Deformation Behavior of the Material under Dynamic Impact Loading
Application of Artificial Neural Networks to Low Cycle Fatigue and Creep Data Processing for Power Plant Materials
Automated Defect Detection in Electron Microscopy with Machine Learning
Data Analytics for Correlative Multimodal Chemical and Functional Imaging
Data Science and the MGI
Deciphering the Atomic Origin of Glasses’ Properties by Machine Learning
Deep Learning and MC-X ray, toward Automatic Sample Segmentation
Digital Protocols for Statistical Quantification of Microstructure Features in Polycrystalline Nickel-based Superalloys
Human-in-the-loop Strategies for Dimensionality Reduction and Optimization in Materials Design
Model-based Reconstruction Algorithms for Time-of-Flight Neutron Tomography
Modeling and Simulation of Rare Events in Multidimensional Spaces
Multi-modal Data Fusion and 3D Reconstruction of Serial Sectioning Data
Neural Networks for Processing of Low Signal-to-noise Data in Scanning Probe Microscopy
P2-8: Evaluation for the Quality of Flake Graphite Cast Iron and Spheroidal Graphite Cast Iron by Tapping Test with Using Artificial Intelligence
Phase Field Regularization for Optimal Grain Reconstruction of Noisy Images
Python For Glass Genomics (PyGGi): A Machine Learning Package to Predict the Properties of Glasses
Recent Advances in 3D Reconstruction Based on Spherical Indexing of EBSD Data
Structure Prediction and Property-based Optimization of Molecular Crystals with GAtor
Workflows for Curation and Analysis of Microstructure-Aware Materials Data: Application to Aging of U-Nb Alloys

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