期刊
CHEMISTRY OF MATERIALS
卷 32, 期 9, 页码 3741-3752出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.chemmater.9b04663
关键词
-
资金
- Toyota Motor Corporation
The performance of solid-state lithium ion batteries can be improved through the use of interfacial coating materials, but computationally identifying materials with sufficiently high lithium-ion conductivity can be challenging. Methods such as ab initio molecular dynamics that work well for superionic conductors can be prohibitively expensive when used on materials that conduct lithium ions less well but are still suitable for use as interfacial coatings. We demonstrate a way to address this problem using machine-learned interatomic potentials models in the form of moment tensor potentials. To prevent the potentials from significantly deviating from density functional theory calculations, we use molecular dynamics simulations coupled with on-the-fly machine learning. This approach increases the efficiency of the calculations by 7 orders of magnitude compared to purely ab initio molecular dynamics, significantly reducing the uncertainty in calculated migration energies and improving agreement with experimentally determined activation energies. Using this approach, we identify two particularly promising materials for use as coatings in batteries as well as several others that are candidates for doping-enhanced ionic conduction.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据