ABSTRACT
Quantum materials, characterized by novel quantum effects, serve as a critical physical foundation for disruptive next-generation technologies such as information technology, quantum computing, and clean energy. However, their complex physical mechanisms pose significant challenges to traditional research methods, rendering material discovery and design time-consuming and costly. To address these challenges, we have developed a suite of AI-empowered methods for quantum materials research. We independently developed the Property Analysis and Simulation Package for materials (PASP), which integrates diverse techniques including the four-state method, effective Hamiltonian methods, Monte Carlo simulations, molecular dynamics (including spin-lattice dynamics), SpinGNN (a neural potential function for magnetic systems), and DREAM (a neural network potential for dielectric systems). Notably, we pioneered a universal foundation model (HamGNN) for predicting electronic structures of complex systems across the entire periodic table. Recently, we also propose a universal model to direct predict electronic density. Building on this foundation, we established the world's first large-scale, AI-based electronic structure database and open online prediction platform (sci-ai.cn), paving the way for the rapid discovery of new materials.
Leveraging these theoretical methods and software tools, we have made significant advances in frontier quantum materials research. In two-dimensional magnetic materials, we revealed the existence of novel strong Kitaev interactions and high-order magnetic interactions. In ferroelectrics, we proposed the concept of fractional quantum ferroelectricity (FQFE), extending ferroelectricity to non-polar crystals—a regime previously considered impossible—thereby breaking through traditional ferroelectric theory. Regarding multiferroics, we established a unified model for spin-order-induced ferroelectricity, clarifying long-standing controversies regarding its physical origin. Looking ahead, AI methods that deeply integrate data-driven approaches with physical principles are poised to become the core engine of quantum materials research, dramatically accelerating the discovery of new materials.
BIOGRAPHY
Hongjun Xiang is a Xie Xide Distinguished Professor in the Department of Physics at Fudan University, a recipient of the National Science Fund for Distinguished Young Scholars, and a Chief Scientist for the National Key R&D Program of the Ministry of Science and Technology. He completed his undergraduate and doctoral studies at the University of Science and Technology of China (1997–2006) and conducted postdoctoral research in the United States (2006–2009). Professor Xiang specializes in computational condensed matter physics, with significant contributions to the modeling of multiferroics and the development of computational methods, including machine learning approaches. His representative achievements include: proposing a universal model for spin-order-induced ferroelectricity, offering a general physical picture for a broad class of multiferroics; establishing the "four-state method" for calculating magnetic interactions and magnetoelectric coupling strength, a technique now widely adopted by research groups globally; breaking the traditional paradigm that ferroelectricity requires polar point groups by proposing the concept of "fractional quantum ferroelectricity"; and developing the commercialized software package PASP for material modeling. His research has been published in leading journals such as Phys. Rev. Lett. (over 40 papers), Science, Nat. Mater., Nat. Comput. Sci., Nat. Mach. Intell., Nat. Commun., and Phys. Rev. X. He has received numerous honors, including ICTP Prize (2018), APS Fellow (2021), Huang Kun Physics Prize (2023), Xplorer Prize (2024), and First Prize of the Shanghai Natural Science Award (2024).
Date & Time
Venue
Chair