Dr. Jufeng Han, currently pursuing a Master’s degree in Artificial Intelligence for Science at the Institute of Semiconductors, Chinese Academy of Sciences, is an emerging researcher specializing in materials informatics and semiconductor and optoelectronic materials. His academic foundation combines advanced studies in artificial intelligence with applications in materials science, focusing on the integration of data-driven modeling with physical principles. Professionally, he has contributed to innovative research projects, most notably the development of a symbolic–neural hybrid modeling framework for perovskite bandgap prediction—an approach that enhances accuracy and interpretability in photovoltaic material screening. His work has been recognized with a Best Paper Candidate nomination and publication acceptance in Materials Today Energy. Beyond research, he collaborates within interdisciplinary teams at the Institute of Semiconductors, demonstrating leadership in bridging AI methodologies with materials discovery. His research contributions have strengthened the role of AI in accelerating semiconductor innovation, particularly in energy-efficient and sustainable technologies. He is a member of the Association for the Advancement of Artificial Intelligence (AAAI) and maintains a strong academic presence through his Google Scholar profile. Jufeng Han’s combination of technical expertise, academic excellence, and forward-looking research vision positions him as a promising scholar in AI-driven materials science and a deserving nominee for the Best Researcher Award.
Profile: Scopus
Featured Publications
Han, Jufeng*, Bandgap prediction for perovskite materials based on symbolic–neural hybrid modeling. Materials Today Energy, Accepted.
Han, Jufeng*, Symbolic–neural hybrid framework for enhanced interpretability and accuracy in perovskite bandgap prediction. Institute of Semiconductors, Chinese Academy of Sciences, In production.
Han, Jufeng, AI-driven modeling approaches for semiconductor and optoelectronic material discovery. AI for Science Research Series, Under review.