Mr. Jufeng Han | Materials Informatics | Best Researcher Award

Published on by Electrical Engineering

Mr. Jufeng Han | Materials Informatics | Best Researcher Award

Master | Institute of Semiconductors | China

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.

Dr. Swarup Ghosh | Energy Harvesting & Self-Powered Systems | Best Researcher Award

Published on by Electrical Engineering

Dr. Swarup Ghosh | Energy Harvesting & Self-Powered Systems | Best Researcher Award

Assistant Professor | SR University | India

Dr. Swarup Ghosh is an Assistant Professor and Assistant Dean (Research) at the School of Computer Science and Artificial Intelligence, SR University, specializing in computational materials science, condensed matter physics, and AI-driven materials discovery. He earned his Ph.D. in Science from Jadavpur University with a focus on first-principles calculations, following an M.Sc. in Physical Sciences and a B.Sc. in Physics. Dr. Ghosh previously served as a Postdoctoral Research Associate at Jadavpur University and as a faculty member at Sammilani Mahavidyalaya, contributing to advanced computational materials research and student mentorship. His work spans density functional theory, molecular dynamics, many-body perturbation theory, electronic structure simulations, and machine-learning-enabled materials design, resulting in publications in high-impact journals and presentations at prestigious scientific forums. His research includes breakthroughs in 2D and nanomaterials, thermoelectrics, photovoltaics, spintronics, and catalytic systems, emphasizing data-centric scientific innovation. He has been honored with national research fellowships, merit-based academic distinctions, and awards for research excellence, while also serving as a reviewer for reputed international journals and participating in professional training programs and conferences. He maintains a strong scholarly impact, demonstrated by 245 citations, an h-index of 9, and an i10-index of 9, underscoring his growing influence in computational materials science and interdisciplinary research.

Profile: Google Scholar

Featured Publications

Swarup Ghosh*, Predicting photovoltaic efficiency of two-dimensional Janus materials for solar energy harvesting: A combined first-principles and machine learning study. Solar Energy Materials and Solar Cells, Accepted.

Swarup Ghosh*, First-principles study on structural, electronic, optical and photovoltaic properties of Sc₂C-based Janus MXenes for solar cell applications. Materials Today Communications, Accepted.

Swarup Ghosh, Predicting band gaps of ABN₃ perovskites: An account from machine learning and first-principles DFT studies. RSC Advances, Accepted.