Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes

Feng Li; Xiaobin Cheng; Lei-Lei Lu; Yi-Chen Yin; Jin-Da Luo; Gongxun Lu; Yu-Feng Meng; Hongsheng Mo; Te Tian; Jing-Tian Yang; Wen Wen; Zhi-Pan Liu; Guozhen Zhang; Cheng Shang; Hong-Bin Yao

doi:10.1021/acs.nanolett.2c00187

Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes

Nano Lett. 2022 Mar 23;22(6):2461-2469. doi: 10.1021/acs.nanolett.2c00187. Epub 2022 Mar 4.

Authors

Feng Li¹, Xiaobin Cheng², Lei-Lei Lu¹, Yi-Chen Yin³, Jin-Da Luo³, Gongxun Lu⁴, Yu-Feng Meng¹, Hongsheng Mo³, Te Tian¹, Jing-Tian Yang³, Wen Wen⁵, Zhi-Pan Liu^{6

7}, Guozhen Zhang¹, Cheng Shang^{6

7}, Hong-Bin Yao^{1

3}

Affiliations

¹ Division of Nanomaterials and Chemistry, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China.
² Department of Chemical Physics, Hefei Science Center of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China.
³ Department of Applied Chemistry, Hefei Science Center of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China.
⁴ College of Materials Science and Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
⁵ Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China.
⁶ Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China.
⁷ Shanghai Qi Zhi Institute, Shanghai 200030, China.

PMID: 35244400
DOI: 10.1021/acs.nanolett.2c00187

Abstract

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).

Keywords: Li metal anode compatibility; all-solid-state batteries; layered halide electrolytes; machine learning; neural network.