Advances and Prospects of Artificial Intelligence in Materials Science: A Bibliometric Analysis Based on Web of Science Core Database (2005-2024)

This study aims to examine the current state and research hotspots of artificial intelligence (AI) applications in materials science over the past two decades (2005–2024), from a bibliometric perspective. Publication volume, citation frequency, H-index, keyword co-occurrence, and other bibliometric indicators were analyzed using data retrieved from the Web of Science Core Collection. The results reveal exponential growth in publications, particularly after 2017, reflecting the increasing maturity and adoption of AI technologies in this field. China leads in publication output, while the U.S. demonstrates higher citation impact per article, indicating complementary strengths in productivity and influence. Institutions such as the Chinese Academy of Sciences and the U.S. Department of Energy emerge as major contributors. Thematic analysis identifies four core research directions: computational design, microstructural analysis, materials modeling, and AI methodologies. Recent trends show a shift toward novel materials discovery, advanced AI techniques, and intelligent systems. This study offers data-driven insights into the evolution of AI-driven materials research and informs future research and international collaboration strategies.