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Meeting MS&T22: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title Deep Learning-based Algorithms for X-ray Microtomography Analysis: Unravelling Challenges for 4D Experiments
Author(s) Hamid Torbatisarraf, Nikhilesh Chawla
On-Site Speaker (Planned) Nikhilesh Chawla
Abstract Scope X-ray microtomography is an important 3D characterization technique due to its non-destructive nature which provides time-dependent (4D) information. However, image processing and segmentation of 4D tomographic data is extremely time intensive. Moreover, factors such as phase transformation or defect propagation during a time-evolved tomography experiment limits the scan time and/or number of scan iterations. Thus, there is a need to establish a robust algorithm that can render time-dependent x-ray datasets accurately and efficiently. In this talk, we describe the application of Deep Convolutional Neural Network (DCNN) algorithms for X-ray image quality enhancement and segmentation. Using a modified Generative Adversarial Network (GAN) algorithm we provide a workflow to transform low quality x-ray tomography acquired by a fast scan to a high-quality dataset. Our results point to the ability to drastically reduce x-ray data acquisition times, thereby opening a window for efficient 4D experiments.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Feasibility Study of Machine Learning-assisted Alloy Design Using Wrought Aluminum Alloys as An Example
AI-enabled Platform for Autonomous Experimentation and Materials Discovery
Are Process-Structure-Property Relationships Useful for Materials Design?
A.I. Driven Sustainable Aluminum Alloy Design
B-1: Autonomous Closed Loop Synthesis of Gold Nanorods via a Modular Chemical-Handling Robotic Platform
B-2: Logistics Box Recognition in Robotic Industrial De-palletizing Procedure with Systematic RGB-D Image Processing Supported by Multiple Deep Learning Method
Data-driven Search for Promising Intercalating Ions and Layered Materials for Metal-ion Batteries
De Novo Inverse Design of Nanoporous Materials by Machine Learning
De Novo Molecular Drug Design Using Deep and Reinforcement Learning
Deep Learning-based Algorithms for X-ray Microtomography Analysis: Unravelling Challenges for 4D Experiments
Deep Learning Approaches for Accelerating Polymer Characterization
Deep Neural Networks for Laser Absorptivity Prediction from Synchrotron X-ray Images
Estimation of Sub-micron Carbide Sizes and Morphologies in Dual-Phase Steels from Light Optical Micrographs Using Generative Adversarial Networks
High-dimensional Neural Network Potential for Liquid Electrolyte Simulations: Applications to Li-ion Battery Materials
Machine Learning Enabled Stacking Fault Energy Prediction in Concentrated Alloys
Machine Learning for Accelerated Defect Dynamics in Materials
Machine Learning Guided Prediction of Rupture Time of 347H Stainless Steel
Multi-Fidelity Machine Learning for Perovskite Discovery
Multi-property Graph Networks for Novel Materials Discovery
Neural Network Prediction of Dynamical Electron Back-Scattered Diffraction Patterns Based on Kinematical Patterns
Phase Identification by Neural Networks Trained from Experimental and Theoretical Structure Data
Physics-informed Machine Learning for Selected Area Electron Diffraction Data Analysis
Rapid Metallic Alloy Development Leveraging Machine Learning
Real-time and Large FOV Ptychography through AI@Edge
Understanding Atomic-scale Mechanisms of Defect Dynamics in Rare Earth Nickelates by Machine Learning and Quantum Simulations

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