| E-MRS(European Materials Research Society) | |||||
| 작성자 | 김** | 작성일 | 2022-05-03 | 조회수 | 111 |
|---|---|---|---|---|---|
|
Oral Presentation Pham thi thanh tan lien Structure and dynamics of glassy and liquid anti-perovskite electrolyte Na3OCl by first-principles and machine learning modeling
Resume : Sodium rich solid electrolyte for Na ion battery, which is notoxic, nonflammabe, widely available, is a potential alternatives to traditional organic liquids. The alkali metal rich an- tiperovskite are among one of the most promising Li and Na solid electrolyte materials. There have been many efforts to increase its ionic conductivity and chemical stability. Li rich antiperovskite has shown excellent ionic conductivities as high as 1 mS.cm ?1 at room temperature and 10mS.cm ?1 at 250 ? C. Although, the Na-rich antiperovskite (NRAP) has a lower ionic conductivity of 0.22 mS.cm ?1 at 240 ? C, researchers have improved the Na conductivity by elemental substitution [1]. Goodenough and Braga groups have reported that amorphous Na antiperovskites are superionic conductors with conductivity as high as 100 mS.cm ?1 at around 150? C, far above the values obtained with other state-of-the-art solid electrolytes [2]. In this work, we shed light into the structural properties, electronic structure and ions dynamics properties of NRAP, firstly, by means first-principles molecular dynamics (FPMD) simulations. In particular, a series of Na 3 OCl-based systems will be the target of this work where we focus our attention on both their glassy and liquid states. The second part will be focusing on how to exploit the obtained FPMD database in order to build proper machine learned potentials to complement our work with the aim to enlarge the the size and time scales of our simulations. |
|||||
