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Meeting MS&T21: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title A Deep Generative Model for Parametric EBSD Pattern Simulation
Author(s) Zihao Ding, Marc De Graef
On-Site Speaker (Planned) Zihao Ding
Abstract Scope Currently, the mainstream approach for electron backscatter diffraction (EBSD) pattern simulation is through a physics-based forward model, which calculates backscattered electron trajectories using Monte Carlo and dynamical scattering simulations and then generates patterns using a gnomonic projection. For each simulation, the result is specific to the given set of parameters. It has been shown that a deep neural network is able to extract features from EBSD patterns and predict various characteristics. We propose a deep generative model that combines a conditional variational autoencoder with a generative adversarial network to realize analytic and parametric EBSD pattern simulation. Compared with the conventional forward model, the deep generative model summarizes a distribution over multiple parameters. The accuracy and quality of the generated patterns can be analyzed by accepted indexing methods.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Deep Generative Model for Parametric EBSD Pattern Simulation
Aluminum Alloy Design Using Physics Informed Machine Learning
De Novo Inverse Design of Nanoporous Materials by Machine Learning
Deep Learning and Uncertainty Quantification for Automated Experiments
Discovery of Novel Crystal Structures via Generative Adversarial Networks
Improving EBM NIR Image Analysis for Component Qualification a Statistical Learning Approach
Machine-learning Based Algorithms for 4D X-ray Microtomographic Analysis
Machine Learning for Automated Experiment in Scanning Probe and Electron Microscopy
Machine Learning Polymer Property Prediction Models with Polymers Represented as Natural Language
Non-iterative Deep Learning for High-fidelity Microscopic Tomography
Optimizing the Training of Convolutional Neural Networks for Image Segmentation
Prediction of Dynamic Properties of LiF and FLiBe Molten Salts with DeepPot Network Potentials
Refinements to the Production of Machine Learning Interatomic Potentials
Semantic Segmentation of Porosity in In-situ X-ray Tomography Data Using FCNs
Tuning Optoelectronic Properties of Semiconductors with First Principles Modeling and Machine Learning
Understanding the Composition–property Relationship of Glasses Using Interpretable Machine Learning

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