汇报标题(Title)：机械进建助力物性模拟与资料设计
汇报人(Speaker)：张云蔚 副教授（榆林大学物理学院）
汇报功夫(Time)：2023年7月7日 (周五) 10:30
汇报地址(Place)：校本部 E106
约请人(Inviter)：任伟 教授
主办部门：理学院物理系
提要(Abstract)：
In modern computational materials science, great efforts have been made to develop simulation methods, e.g., density functional theory (DFT) and molecular dynamics. These simulation methods can help researchers understand mechanisms, predict properties and design new materials. Despite these successes, there remain multiple experimental phenomena that can hardly be described by conventional atomistic/molecular simulation methods, which severely impede us from further understanding and designing advanced functional materials. Recently, computational materials science is undergoing a second revolution empowered by machine learning (ML). ML methods do not exclusively rely on the theoretical understanding of materials but take a data-driven approach to solve the problems. In this talk, I will report my recent works on applying ML to predict the notorious properties of materials, i.e. lifetime of Li-ion batteries and high-temperature superconductivity, which are challenging for conventional simulation methods.
