Automated Burgers Vector Identification for Individual Dislocations in Bulk Crystals

Abstract

Weak-beam imaging in dark-field X-ray microscopy (DFXM) can resolve individual dislocations in bulk crystals, but assigning Burgers vectors from the resulting contrast typically requires manual comparison with forward simulations. Here, we train a physics-informed convolutional neural network (CNN) on geometrical optics simulations of isolated dislocations in face-centred cubic (FCC) aluminium, incorporating crystallographic constraints into the learning pro- cess. The model identifies Burgers vectors from weak-beam integrated rocking-curve images. On synthetic test data, the model achieves an accuracy of approximately 93%. In an experimental cross-slip case, the constrained model as- signs 72.7% of the layer-wise predictions to the reference Burgers vector. These results show that simulation-trained, physics-informed CNNs represent a step toward automated dislocation identification in DFXM.