Materials are a fundamental cornerstone of economic and social development, and the integration of artificial intelligence (AI) technologies has opened new opportunities for research in materials science. This paper reviews the advancements and innovative applications of data and large AI models in the field of materials. It begins by discussing the progress and trends in materials science databases, highlighting internationally renowned databases and their applications, along with case studies showcasing data-driven materials research and development. The paper then explores prediction models for material properties, force fields, and Hamiltonians, emphasizing the critical roles of various AI models and platforms in materials research. Furthermore, it examines the applications of language models, knowledge graph technology, and enhanced search capabilities in materials science, such as vertical language models supporting research, knowledge graphs uncovering latent connections, and enhanced search optimizing information retrieval. Detailed examples include innovative applications like MatChat, developed by the Chinese Academy of Sciences, for predicting compound synthesis pathways, and GPTFF, a large AI model for force field predictions. Finally, the paper reflects on the future development of data and AI technologies in materials science. Although challenges remain in advancing these technologies, the field holds immense potential, and their applications are set to drive further progress in materials science research and industrial development.