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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 Non-iterative Deep Learning for High-fidelity Microscopic Tomography
Author(s) Singanallur Venkatakrishnan, Amir Koushyar Ziabari, Jacob Hinkle, Micheal Kirka , Jeffrey Warren, Hassina Bilheux, Vincent Paquit, Ryan DeHoff
On-Site Speaker (Planned) Singanallur Venkatakrishnan
Abstract Scope Microscopic computer tomography (CT) is a ubiquitous technique for 3D non-destructive characterization of samples from the nano-meter to micron length scales. However, obtaining high quality 3D reconstructions can be challenging because of the low signal-to-noise ratio, the sparse angular sampling dictated by realistic experiment times, and the complex physics associated with the measurements. In this talk, we will present the development of non-iterative deep-learning-based reconstruction algorithms for microscopic CT data. We will discuss how different network architectures and sources of limited training data (reference samples, single step from a time-resolved scan, and computer-aided design models) can be leveraged to obtain high quality reconstructions from X-ray and neutron CT instruments. These improvements in image quality offered by the deep-learning approach leads to a more efficient use of instrument time enabling faster scans and higher spatio-temporal resolution for time-resolved experiments.
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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