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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 Real-time and Large FOV Ptychography through AI@Edge
Author(s) Anakha Babu, Tao Zhou, Saugat Kandel, Yi Jiang, Yudong Yao, Sinisa Veselli, Zhengchun Liu, Tekin Bicer, Martin V Holt, Antonino Miceli, Mathew J Cherukara
On-Site Speaker (Planned) Tao Zhou
Abstract Scope X-ray ptychography is a high-resolution imaging technique that relies on the oversampling of the real space information with scattering from a coherent x-ray beam. We present PtychoNN, a novel approach to solve the phase retrieval problem based on deep convolutional neural networks. Trained on experimentally reconstructed data, PtychoNN is both accurate and sample-beamline agnostic. It is capable of predicting phase at a speed up to 100 times faster compared to conventional iterative methods, achieving phase retrieval in real time. Moreover, PtychoNN infers on single shot diffraction patterns rather than an ensemble of data, thus removing the oversampling constraints in ptychography while enlarging its field of view in the process.We shall present results from a recent demonstration of PtychoNN at the edge, performed on the HXN beamline at the APS. We show live stitching of large field-of-view ptychography at 2.5 Hz and real time phase retrieval at 100 Hz.

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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