  Star scientists are highly influential researchers who have made significant
contributions to their field, gained widespread recognition, and often
attracted substantial research funding. They are critical for the advancement
of science and innovation, and they have a significant influence on the
transfer of knowledge and technology to industry. Identifying potential star
scientists before their performance becomes outstanding is important for
recruitment, collaboration, networking, or research funding decisions. Using
machine learning techniques, this study proposes a model to predict star
scientists in the field of artificial intelligence while highlighting features
related to their success. Our results confirm that rising stars follow
different patterns compared to their non-rising stars counterparts in almost
all the early-career features. We also found that certain features such as
gender and ethnic diversity play important roles in scientific collaboration
and that they can significantly impact an author's career development and
success. The most important features in predicting star scientists in the field
of artificial intelligence were the number of articles, group discipline
diversity, and weighted degree centrality. The proposed approach offers
valuable insights for researchers, practitioners, and funding agencies
interested in identifying and supporting talented researchers.
