锂(药物)
电解质
标杆管理
电池(电)
离子
计算机科学
计算模拟
材料科学
生化工程
化学
计算科学
有机化学
热力学
工程类
物理
医学
功率(物理)
电极
物理化学
营销
业务
内分泌学
作者
Piotr Jankowski,W. Wieczorek,Patrik Johansson
标识
DOI:10.1007/s00894-016-3180-0
摘要
SEI-forming additives play an important role in lithium-ion batteries, and the key to improving battery functionality is to determine if, how, and when these additives are reduced. Here, we tested a number of computational approaches and methods to determine the best way to predict and describe the properties of the additives. A wide selection of factors were evaluated, including the influences of the solvent and lithium cation as well as the DFT functional and basis set used. An optimized computational methodology was employed to assess the usefulness of different descriptors.
科研通智能强力驱动
Strongly Powered by AbleSci AI