报告题目：Theoretical design of Li-rich antiperovskite-type solid electrolytes for all-solid-state batteries by high-throughput first-principles calculations and machine learning approaches

报告人：Randy Jalem，National Institute for Materials Science,Japan 研究员

报告时间：2023年8月3日15:30

报告地点：中心校区唐敖庆楼B521报告厅

报告摘要

　　Solid electrolyte (SE) materials with exceptionally high Li ionic conductivity (10-3 S/cm or higher) are strongly sought for the development of highly-practical all-solid-state batteries (ASSBs). Li-rich antiperovskites have been reported to show a great potential for SE use, in particular, in terms of ionic conductivity, electrochemical stability, mechanical softness to realize an intimate contact with electrodes and other battery materials in the cell for reduced cell internal resistance, interface stability vs. Li metal via formation of electronic-insulating but ionic-conducting decomposition interphase layer, and rapid synthesizability with high scalability. Meanwhile, the in-silico design of inorganic materials by high-throughput first-principles calculations and machine-learning techniques has been one of the employed approaches in recent years, aiding experimentalists in determining highly promising and novel chemistries for various applications. In this talk, a large-scale computational SE-screening study on a chemical space of >10,000 Li-rich antiperovskites with tetragonal and cubic structures by high-throughput density functional theory and machine learning methods will be presented. Experimental efforts related to the actual synthesis and characterization of some of the theoretically designed novel antiperovskites that are predicted to be highly-promising for SE use will also be highlighted. Perspectives and remaining issues on the use of Li-rich antiperovskite SEs for ASSBs, as well those related to the computational and data-driven material design of SEs as a whole will be discussed.

报告人简介

　　Dr. Randy Jalem is from the Philippines, he received his PhD in Materials Science and Engineering in 2014 from Nagoya Institute of Technology, Japan. His PhD study was focused on the physics-driven computational modeling of lithium/sodium ion battery materials, one specific area that he studied is on the development of computational techniques that combine both ab initio DFT and experimental data to accelerate battery material screening and exploration. In 2015, he moved to the National Institute for Materials Science, Japan, or NIMS, as a postdoctoral researcher, focusing on the computational design of Li- and Na-based solid electrolytes for all-solid-state batteries. In 2016, he became a Principal Investigator in the Japan Science and Technology PRESTO Project under the Materials Informatics Region, the research topic was about the informatics-based computational search for new Li ion conductive ceramics, leveraging from the knowledge pool provided by crystal structure databases and then extending to “non-stoichiometry” chemical search spaces. Since 2017, he has been working as a Staff Researcher at NIMS, Japan in the Interface Electrochemistry Group, and is still continuing his computational battery research works.

举办单位：

吉林大学物理学院

物质模拟方法与软件教育部重点实验室