报告题目 (Title)：Advancing Orbital-Free DFT and DFTB for Large-Scale ab initio Materials Modeling with Machine Learning（利用机器学习改进大规模从头算材料模拟方法：无轨道密度泛函理论和紧束缚密度泛函理论）

报告人 (Speaker)：Assoc. Prof. Sergei Manzhos（Tokyo Institute of Technology）

报告时间 (Time)：2024年9月5日(周四) 15:30-17:30

报告地点 (Place)：校本部G313

邀请人 (Inviter)：任伟 教授

主办部门：理学院物理系

摘要 (Abstract)：

Ab initio materials modeling is still largely based on Kohn-Sham DFT (density functional theory). The near-cubic scaling of KS-DFT makes possible routine calculations only at small scale, limited to 102-3 atoms. This becomes problematic when ab initio level insight (effects of or on electronic structure, mechanisms of various phenomena) is needed for intrinsically large-scale problems (e.g. microstructure effects, large molecules and interfaces). Large-scale DFT-based methods exist (Order-N DFT, Orbital-free (OF) DFT, DFTB (density functional tight binding)) but still require improvements to be routinely usable in various applications. In this talk, I will consider two large-scale approaches, OF-DFT and DFTB, and specifically focus on using machine learning (ML) to improve them, either to improve the accuracy or to extend the field of applicability. For DFTB, I will show how one can realize a QM-MM (quantum mechanics-molecular mechanics) hybrid not by spatial range but by type of interactions, modeling some of the interatomic interactions at the MM level with a ML-optimized potential function. For OF-DFT, I will show how ML can be used to help construct kinetic energy functionals (KEF) which have been the bottleneck on the way to wider adoption of the OF-DFT method that is capable of routine modeling of million-atom systems.