Speaker
Hongbin Zhang
Institute of Materials Science, Technical University of Darmstadt
Date＆Time
2019.08.2 (Fri) AM 9:30
Location
Aoqing Tang Building, C603
Abstract
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.
