ChatXRD: A LLM‐Driven Framework for Crystal System Classification and Lattice Parameters Prediction Based on XRD

ABSTRACT High‐throughput X‐ray diffraction (XRD) analysis is critical for accelerating material discovery, but traditional methods often require significant manual interpretation. We propose ChatXRD, an innovative LLM‐driven framework that integrates a GPT‐5‐driven agent with custom‐designed tools to autonomously perform the task of crystal system classification and lattice parameters prediction. In this framework, we have meticulously designed two tools, CrystalSystemClassifier and LatticeParametersPredictor, which are based on a self‐attention‐decayed transformer model tailored for XRD data. Our method achieves over 97% accuracy in crystal system classification and values between 0.88 and 0.99 in lattice parameter prediction, surpassing the performance of current state‐of‐the‐art approaches. Moreover, by adopting decision tree‐based reasoning, the accuracy and reliability of task execution are further improved. These results demonstrate the potential of combining domain‐specific neural models with LLM‐based reasoning for automated and interpretable XRD analysis. This framework enables scalable high‐throughput XRD analysis, paving the way for future advances in automated materials research and crystallography.