Accurate identification and characterization of defects in materials enabled by deep learning image analysis
3-year fixed-term contract
Transmission electron microscopy (TEM) allows detailed study of materials at the atomic scale, revealing defects and structures. Many objects such as voids, second-phase precipitates, stacking fault tetrahedra, and radiation-induced segregation on grain boundaries or dislocation loops etc can be seen in a TEM micrograph, leading to complex and numerous data. Manual analysis is error-prone and time-intensive. While semi-automatic methods have limitations, artificial intelligence offers a tailored solution for analyzing these complex images. For two years, we have been working on a workflow designed for TEM analysis using deep learning tools. The new recruit will create a workflow that incorporates previous work and new advancements. Two main areas are addressed: first, using computer vision and deep learning like Yolo and Mask R-CNN for TEM image analysis to detect, track, follow and classify objects; and second, employing TEM tomography to generate 3D visualizations from multiple angled images, a task made efficient by our novel segmentation approach using a convolutional auto-encoder. Finally, great attention will be given to the database design of the experimental information.