1.National Engineering Research Center of Light Alloy Net Forming，Shanghai Jiao Tong University, Shanghai 200240，China
2.State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China
Data-driven approaches have become an important trend in materials research. The emergence of large language models not only provides a potential solution for unstructured data processing in the field of materials science, but also, through their powerful language understanding and generation capabilities, facilitates knowledge discovery, automated analysis, improved interpretability, and multi-module collaborative operations, thus enhancing research efficiency and driving breakthroughs in materials science. This article explores the theoretical foundations of large language models, examines their key functions, optimization methods, and applications in materials science. In particular, large language models can effectively process and extract key information from unstructured texts, helping to accelerate material discovery and design. This article also envisions the future development of large language models in the integration of artificial intelligence and materials science, highlighting their significant potential in advancing the automation and intelligence of materials research.