Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes.
basic_science · Level V
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- Record sourced from PubMed, PMID 35244400.
- Also identified by DOI 10.1021/acs.nanolett.2c00187.
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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 Li<sub>2</sub>ZrCl<sub>6</sub> and Li<sub>2</sub>HfCl<sub>6</sub>, for stable ASSLMBs. The predicted halide SEs possess high Li<sup>+</sup> 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 cm<sup>2</sup>), high average Coulombic efficiency (∼99.48%), good rate capability (63 mAh g<sup>-1</sup> at 1.5 C), and unprecedented cycling stability (87% capacity retention for 70 cycles at 0.5 C).