Bridging language models and computational materials science: A prompt-driven framework for material property prediction

Abstract

Large language models (LLMs) have demonstrated effectiveness in interpreting complex data. However, they encounter challenges in specialized applications, such as predicting material properties, due to limited integration with domain‐specific knowledge. To overcome these challenges, we introduce MatAgent, an artificial intelligence (AI) agent that combines computational chemistry tools, such as first‐principles (FP) calculations, with the capabilities of LLMs to predict key properties of materials. By leveraging prompt engineering and advanced reasoning techniques, MatAgent integrates a series of tools and acquires domain‐specific knowledge in the field of material property prediction, enabling it to accurately predict the properties of materials without the need for predefined input structures. The experimental results indicate that MatAgent achieves a significant improvement in prediction accuracy and efficiency. As a novel approach that integrates LLMs with FP calculation tools, MatAgent highlights the potential of combining advanced computational techniques to enhance material property predictions, representing a significant advancement in computational materials science.