报告题目：In-silico design of magnetic materials

报 告 人：HongbinZhang，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 ingreen energy applications as they provide efficient ways ofharvesting/converting energies and engineering spintronic devices with lowenergy cost. The key questions nowadays are how to optimize the performance ofexisting systems and to design novel materials for broader applications. In thistalk, we will present our recent results on high throughput screening andmachine learning of magnetic materials. Using the in-house developed highthroughput environment, the stabilities of antiperovskite, MAX, and quaternaryHeusler compounds are investigated, resulting in many potential candidates withinteresting physical properties for further experimental exploration.Furthermore, we applied machine learning techniques to model the Curietemperature of magnetic materials, where explicit evaluation based on densityfunctional 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 reliablepredictions, particularly with the help of combined high throughput and machinelearning methods.

举办单位：尊龙凯时物理学院

计算物理方法与软件创新中心

超硬材料国家重点实验室

尊龙凯时省物理学会