报告摘要：Understanding the origin and evolution of molecular complexity in the interstellar medium is one central problem in astrophysics and astrochemistry. This field has advanced through traditional approaches, including chemical reaction networks, rate-equations, gas-grain modeling, quantum mechanical calculations, and radiative-transfer-based spectral analysis. These methods have provided the essential foundation for interpreting how molecules form and evolve in different astrophysical environments. However, with the rapid growth of modern observational data from facilities such as ALMA and JWST, the field is increasingly challenged by severe line confusion, incomplete reaction networks, and the difficulty of extracting robust physical and chemical information from complex spectra.In this talk, I will present our recent efforts to build on traditional modeling and incorporate AI-enabled methods for exploring molecular complexity in the interstellar medium. This framework combines physics-based modeling with new data-driven and machine-learning tools for reaction-network expansion, multiphase chemical evolution, and automated spectral-line identification. I will discuss how recent developments such as ChemiVerse, GraSSCoL, and Spectuner can be integrated with traditional modeling strategies to build a more scalable and interconnected framework linking reaction pathways, abundance evolution, synthetic spectra, and observational inference. By combining traditional modeling with AI-enabled methods, we aim to improve the efficiency and reliability of molecular identification and to support more systematic studies of molecular complexity in the interstellar medium. The broader goal is to develop a more predictive framework for understanding how complex, and potentially prebiotic, molecules form and evolve in space.
主讲人简介：Donghui Quan is Professor of Physics in the School of Mathematics and Physics at Xi’an Jiaotong-Liverpool University. He received his B.S. and M.S. degrees from the University of Science and Technology of China, and his Ph.D. in Chemical Physics from The Ohio State University. He also carried out postdoctoral research at the University of Kentucky, and previously held positions at Eastern Kentucky University, Xinjiang Astronomical Observatory of the Chinese Academy of Sciences, and Zhejiang Lab. His research has been focused on astrophysics and astrochemistry for over two decades. In recent years, he has been working on the use of intelligent computing to empower scientific discovery, with particular interest in the formation of molecules in the universe and the chemical origins of life. He has led a research team that has made a series of advances in interstellar molecular modeling, AI-driven chemical reaction prediction, automated spectral-line identification, and large-model development for astronomy. He has served as PI or lead investigator on more than ten major national and regional research projects, and has developed intelligent research platforms and models including ChemiVerse, Spectuner, and GraSSCoL. He has published more than 70 papers in leading journals such as ApJS, MNRAS, and A&A, and has made notable contributions at the intersection of intelligent astronomy and astrophysics.
