Apr 26, 2024
10:30am - 11:00am
Room 441, Level 4, Summit
Kory Burns1,Caitlin Kohnert2,Khalid Hattar3
University of Virginia1,Los Alamos National Laboratory2,The University of Tennessee, Knoxville3
Kory Burns1,Caitlin Kohnert2,Khalid Hattar3
University of Virginia1,Los Alamos National Laboratory2,The University of Tennessee, Knoxville3
Simulating reactor conditions inside a transmission electron microscope (TEM) gives insight into the rate of defect formation and survivability of materials in the environments in which they were designed to perform. Efforts to fully encapsulate dynamic processes occurring in a material have been challenging, owing to the large volume of information collected from an individual experiment and multiple interactions occurring simultaneously. Hereby, deep neural networks emerge as a suitable method to extract complicated information from input images and output useful analytics that help understand the physics of a reaction. This talk discusses strategies for handling a range of datasets from <i>in situ</i> TEM experiments that span different material systems, imaging conditions, and irradiation conditions to predict the stability of materials. Emphasis will be placed on developing end-to-end process flows equipped with generating synthetic data to learn feature representations of images, pattern recognition to highlight hidden trends in the data, and overall implications for the advancement of nuclear materials characterization.