MatPC: Prompting Large Language Model, Crystal Structure Prediction, and First-Principles for Semantic-Driven Material Design

In this study, an AI-guided framework is developed for semantic-driven material design, integrating large language models (LLMs) with first-principles methods and crystal structure prediction (MatPC) to identify novel photovoltaic materials. By utilizing prompt-engineered LLMs, semantic embeddings of material property descriptions are leveraged to identify uncommon materials candidates with strong alignment to desired functionalities. The material discovery pipeline combines LLMs, similarity scoring, dimensional reduction, formula screening, crystal structure prediction, and DFT validation into a cohesive computational workflow. The candidates undergo crystal structure prediction to generate polymorphs using a hybrid genetic algorithm-graph neural network (GA-GNN) approach, followed by validation through DFT calculations on atomic and electronic properties, optical absorption, and theoretical power conversion efficiencies. As a case study, an unconventional Bi 2 WO 6 polymorph is identified as a promising photovoltaic material, with its electronic and optical properties thoroughly analyzed via first-principles calculations. Our study presents an efficient material discovery pipeline leveraging large language models (LLMs) to accelerate the material design process.