Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes
收藏NIAID Data Ecosystem2026-03-13 收录
下载链接：
https://figshare.com/articles/dataset/Stable_All-Solid-State_Lithium_Metal_Batteries_Enabled_by_Machine_Learning_Simulation_Designed_Halide_Electrolytes/19307947
下载链接
链接失效反馈官方服务：
资源简介：
Solid
electrolytes (SEs) with superionic conductivity and interfacial
stability are highly desirable for stable all-solid-state Li-metal
batteries (ASSLMBs). Here, we employ neural network potential to simulate
materials composed of Li, Zr/Hf, and Cl using stochastic surface walking
method and identify two potential unique layered halide SEs, named
Li2ZrCl6 and Li2HfCl6,
for stable ASSLMBs. The predicted halide SEs possess high Li+ conductivity and outstanding compatibility with Li metal anodes.
We synthesize these SEs and demonstrate their superior stability against
Li metal anodes with a record performance of 4000 h of steady lithium
plating/stripping. We further fabricate the prototype stable ASSLMBs
using these halide SEs without any interfacial modifications, showing
small internal cathode/SE resistance (19.48 Ω cm2), high average Coulombic efficiency (∼99.48%), good rate
capability (63 mAh g–1 at 1.5 C), and unprecedented
cycling stability (87% capacity retention for 70 cycles at 0.5 C).
创建时间：
2022-03-04
