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About this Symposium
Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Organizer(s) Alex Belianinov, Oak Ridge National Laboratory
Ichiro Takeuchi, University of Maryland
Jeff Simmons, Wright Patterson Air Force Research Laboratory
Jason Hattrick-Simpers, National Institute of Standards and Technology
Scope Description:

Advances in scientific hardware, statistical algorithms, as well as easy access to databases and computing power made large volumes of high veracity materials data possible to acquire, store, and process. However, these typically large multimodal datasets conceal pertinent material property information by their size, complexity and noise. This symposium will cover a breadth of topics in material synthesis, characterization, and theory; where modern data analytics approaches play a crucial role in interpretation and discovery. An effort will be made to showcase implementation as well as highlight strengths and weaknesses of various data processing and machine learning methods in real world materials problems. The symposium will also discuss new developments in areas of data reconstruction, denoising, image processing, object tracking, correlative analysis, and data management for end use.

Description Bullets:
• Multimodal data acquisition
• Signal processing via adaptive, compressive, and dynamic sensing
• Machine learning to extract materials data
• Data fusion, image processing and object tracking
• Scalable data management and computational strategies

Abstracts Due 04/05/2019
Proceedings Plan Definite: At-meeting proceedings

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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