As an optional subsequent step, further optimization of the generated structures can be performed using a unified graph neural network (GNN) force field (FF), such as the atomistic Utilizing the scripts available in JARVIS-Tools, background subtraction is automatically performed. First, the spectrum can be matched with structures from atomic structure databases, such as those in JDFT or similar databases, based on simulated XRD patterns using cosine similarity or other metrics. Alternatively, there are multiple scenarios where the user might (1) not know the constituent elements at all, (2) have some idea about the involved elements, or (3) explicitly know the chemical formula. Based on the provided information, the XRD pattern can be converted to strings followed by tokenization, after which one or more pre-trained DiffractGPT models can be applied to generate potential crystal structures. Subsequently, further optimization can be performed using a unified GNN force field, such as ALIGNN-FF, to generate additional structure candidates. A tentative application for this workflow is available at the website .