掺杂剂
离子电导率
材料科学
电解质
无机化学
锂(药物)
镧
空位缺陷
兴奋剂
离子键合
电导率
密度泛函理论
氧气
氮气
化学物理
费米能级
离子
化学
物理化学
计算化学
结晶学
电极
医学
物理
光电子学
有机化学
量子力学
内分泌学
电子
作者
Jiacheng Wang,Kieran Tay,Nianqiang Wu,Peng Bai
出处
期刊:Meeting abstracts
日期:2023-08-28
卷期号:MA2023-01 (6): 1001-1001
标识
DOI:10.1149/ma2023-0161001mtgabs
摘要
Our recent experiments have shown that nitrogen doping can effectively improve the ionic conductivity of lithium lanthanum titanate (LLTO) electrolytes. Herein, computational studies are performed to study the mechanisms of ionic conductivity with density-functional theory and machine learning approaches. To elucidate the relationship between nitrogen concentration and oxygen vacancy, the energy for nitrogen doping into the lattice and the formation energy for oxygen vacancy were computed. Oxygen vacancies were found more likely to be created at higher level of nitrogen dopants. The calculations also discovered the potential formation of nitrogen bonds, generating vacancy-like pathways for Li + conduction. In addition, the minimum-energy pathways for Li + migration in pristine and various doped LLTO materials were analyzed using the nudged-elastic band method. To correlate energetics and structural features of the different LLTO materials, several machine learning methods were successfully constructed, where the neighboring sites with oxygen vacancies are identified as the major factor influencing the energies and hopping barriers compared with nitrogen dopants. The new insights can guide the design and synthesis of anion dopped oxide electrolytes in solid-state lithium-ion batteries.
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