Leveraging Language Model, Crystal Structure Prediction and First-Principles Calculation for Material Design

Large language models (LLMs) have demonstrated transformative potential for materials discovery in condensed matter systems, but their full utility requires both broader application scenarios and integration with ab initio crystal structure prediction (CSP), density functional theory (DFT) methods and domain knowledge to benefit future inverse material design. Here, we develop an integrated computational framework combining language model-guided materials screening with genetic algorithm (GA) and graph neural network (GNN)-based CSP methods to predict new photovoltaic material. This LLM + CSP + DFT approach successfully identifies a previously overlooked oxide material with unexpected photovoltaic potential. Through transformer-based vector similarity analysis coupled with unsupervised clustering and first-principles calculations, we demonstrate that this material exhibits a direct band gap and high theoretical efficiencies that are suitable for photovoltaic application. Our work highlights a hierarchical computational inverse design pipeline that can efficiently navigate the material space to identify nonintuitive functional materials with tailored optoelectronic properties.