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Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Presentation Title Phase Field Regularization for Optimal Grain Reconstruction of Noisy Images
Author(s) Jeff P. Simmons, Amir Koushyar Ziabari, Lawrence F. Drummy, Charles A. Bouman, Jeffrey Rickman
On-Site Speaker (Planned) Jeff P. Simmons
Abstract Scope In order to increase throughput or reduce beam damage, dosage is often limited in electron microscopy, resulting in a trade-off between dose and image quality. Classical techniques, e.g. median filters, produce artifacts because they are material-unaware. Advanced regularized optimization techniques, such as Maximization of Posterior Marginals or Model Based Iterative Reconstruction dramatically improve the results because they simulate capillarity, but specific material behavior is still not represented. Phase field can be calibrated with atomistic computations and used as a regularizer, with the difficulty that phase field is non-convex, leading to an enormous number of erroneous local minima. We overcame this difficulty by building convexity into the model, allowing for the existence of a single global minimum with no local minima. This talk will present results of SiC polycrystalline materials reconstructed as denoised grains, using a convex phase field model that was calibrated with atomistics potentials fit to SiC.
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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