报告题目：An artificial intelligence era of magnetism

报 告 人：Jiadong Zang University of New Hampshire

报告时间：2026年5月12日 10：00

报告地点：物理楼W361会议室

内容摘要：Magnetic materials play a crucial role in numerous aspects of daily life, yet their choice remains limited, and discovering new ones is highly challenging. In recent years, the emergence of machine learning and artificial intelligence has revolutionized materials discovery, offering new hope for identifying novel functional magnetic materials. However, a comprehensive database of magnetic materials is still lacking. In this talk, we address this challenge by leveraging advanced large language models to extract material properties from experimental data reported in peer-reviewed journal articles. The database currently includes more than 60,000 magnetic materials and is still growing. Our database is highly inclusive and also encompasses superconductors and thermoelectric materials. We hope this resource accelerates materials discovery and paves the way for a new era in magnetism.

报告人简介：Prof. Jiadong Zang received bachelor’s degree in 2007 and PhD degree in 2012, both from Fudan University. He was a postdoctoral fellow in the Institute of Quantum Matter at the Johns Hopkins University during 2012-2015. In 2015, he joined the Department of Physics at the University of New Hampshire (UNH) as an assistant professor. He was promoted to associate professor in 2020, and then to the full professor in 2023. His research field is theoretical condensed matter physics with a focus on many aspects of magnetism, including topological magnetism, quantum transport, and functional magnetic materials. Prof. Zang was recipient of IUPAP Young Scientist Prize in the field of magnetism and the Alexander von Humboldt Fellowship for Experienced Researchers. He was the chair of the APS New England Section.