The application status and development trend of artificial intelligence in chemical science - based on bibliometric and knowledge graph analysis

A systematic analysis based on bibliometric methods and knowledge graph is presented in this study to examine the current landscape and emerging trends of artificial intelligence (AI) in chemistry. Using data retrieved from the Web of Science Core Collection, analyses were conducted on publication trends, contributing countries and regions, research institutions, and keyword co-occurrence. From 1961 to 2024, the application of AI in chemistry has increased substantially and exponentially. China and the United States have emerged as the leading contributors in this field with high publication output, strong citation performance, and a large number of competitive institutions. Additionally, the results of the keyword co-occurrence analysis reveal that there are four key thematic research themes in this field: (1) chemometrics and predictive modeling, (2) computational chemistry and molecular simulation, (3) materials design and performance optimization, and (4) automated laboratory platforms. The temporal evolution of keywords further highlights that automated laboratory platforms, as well as machine learning algorithms and systems integration, remain prominent and rapidly evolving research frontiers in chemistry.