报告题目：In-silico design of magnetic materials

报 告 人 ：Hongbin Zhang，Institute of Materials Science, Technical University of Darmstadt, 64287 Darmstadt, Germany

报告时间：2019年8月2日（星期五）上午9:30

报告地点：前卫南区唐敖庆楼C区603报告厅

报告摘要：Magnetic materials play an essential role in green energy applications as they provide efficient ways of harvesting/converting energies and engineering spintronic devices with low energy cost. The key questions nowadays are how to optimize the performance of existing systems and to design novel materials for broader applications. In this talk, we will present our recent results on high throughput screening and machine learning of magnetic materials. Using the in-house developed high throughput environment, the stabilities of antiperovskite, MAX, and quaternary Heusler compounds are investigated, resulting in many potential candidates with interesting physical properties for further experimental exploration. Furthermore, we applied machine learning techniques to model the Curie temperature of magnetic materials, where explicit evaluation based on density functional theory is a challenging task. The resulting accuracy is as high as 90% with a mean-average-error about 58K. This enables us to make reliable predictions, particularly with the help of combined high throughput and machine learning methods.

举办单位：吉林大学物理学院

　　　　　计算物理方法与软件创新中心

　　　　　超硬材料国家重点实验室

　　　　　吉林省物理学会