Furthermore, the DGPT-generated structure is optimized using ALIGNN-FF, and the corresponding XRD patterns are also presented. We observe that for the defective structure, the peaks show reasonable agreement for peaks before 45 • 2θ, but after that, it begins to differ compared to input XRD pattern. This can be attributed to the fact that the current work has primarily focused on perfect materials with no defective structures explicitly included during training. However, it could be extended to defective materials in the future. Detecting defects, such as vacancies, dislocations or other imperfections, in materials through X-ray diffraction (XRD) is a challenging task. While XRD is commonly used to study crystalline materials, the presence of defects introduces complexities in the diffraction patterns. Previ-ous studies, such as those utilizing convolutional neural networks and Long Short-Term Memory (LSTM) networks for identifying vacancies, strain in semiconductors, have made progress in this area. Our model, trained on diffraction patterns from ideal structures, can be extended to defective systems by incorporating additional training data from materials with known defects. With such data, the model should be able to generalize and capture the diffraction features associated with defects and dislocations.