In this paper, we describe the architecture and training methodology of DiffractGPT and evaluate its performance on the PXRD dataset. DiffractGPT uses transformer architecture based on the Mistral AI model but can be easily adapted to other LLMs as well. We demonstrate that DiffractGPT not only matches the accuracy of traditional methods but also significantly reduces the computational time and expertise required for crystal structure determination. AtomGPT and DiffractGPT are analogous to AlphaFold (mentioned above) in their approach to solving complex structure-property relationships using machine learning. They adapt generative predictive frameworks to tackle fundamental challenges in materials science, mirroring what AlphaFold 40 has achieved for biology. The results show the promise of using generative machine learning models for automating the crystal structure determination process, opening up new avenues for materials discovery and design. The code used in this study will be made available on the AtomGPT GitHub page: .