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Found 6 papers, 3 papers with code
Machine learning for interpreting coherent X-ray speckle patterns
Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from images is challenging.
Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs
In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model.
A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations
The system provides analysis of features observed in TEM including both static and dynamic properties using the YOLO-based defect detection module coupled to a geometry analysis module and a dynamic tracking module.
Multi defect detection and analysis of electron microscopy images with deep learning
Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis.
Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation
The use of accurate scanning transmission electron microscopy (STEM) image simulation methods require large computation times that can make their use infeasible for the simulation of many images.
Assessing Graph-based Deep Learning Models for Predicting Flash Point
To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules.
