Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging

Abstract

X-ray Bragg coherent diffraction imaging (BCDI) is widely used for materials characterization. However, obtaining X-ray diffraction data is difficult and computationally intensive. Here, we introduce a machine learning approach to identify crystalline line defects in samples from the raw coherent diffraction data. To automate this process, we compose a workflow coupling coherent diffraction data generation with training and inference of deep neural network defect classifiers. In particular, we adopt a continual learning approach, where we generate training and inference data as needed based on the accuracy of the defect classifier instead of all training data generated a priori. The results show that our approach improves the accuracy of defect classifiers while using much fewer samples of data.