2022
DOI: 10.1021/acs.nanolett.2c00187
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Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes

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 2 ZrCl 6 and Li 2 HfCl 6 , for stable ASSLMBs. The predicted halide SEs possess high Li + conductivity and outstanding compatibility with Li metal anodes. We syn… Show more

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Cited by 52 publications
(34 citation statements)
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“…More detailed studies are still required to understand the heterointerface between halide and sulfide SSEs. Counterintuitively, Yao et al ( 88 ) recently reported Li-stable Li 2 ZrCl 6 and Li 2 HfCl 6 . They claimed that the thick LiCl film formed between Li 2 ZrCl 6 and Li, Li/Li 2 ZrCl 6 /Li symmetric cells demonstrated superior stability against Li metal anodes with 4000 hours of steady lithium plating/stripping at 0.1 mA cm −2 .…”
Section: Interfaces Of Halide Assbsmentioning
confidence: 99%
“…More detailed studies are still required to understand the heterointerface between halide and sulfide SSEs. Counterintuitively, Yao et al ( 88 ) recently reported Li-stable Li 2 ZrCl 6 and Li 2 HfCl 6 . They claimed that the thick LiCl film formed between Li 2 ZrCl 6 and Li, Li/Li 2 ZrCl 6 /Li symmetric cells demonstrated superior stability against Li metal anodes with 4000 hours of steady lithium plating/stripping at 0.1 mA cm −2 .…”
Section: Interfaces Of Halide Assbsmentioning
confidence: 99%
“…(C) Training data set for the Li–Zr–Cl NN potential training. Reproduced with permission 164 . Copyright 2022, American Chemical Society.…”
Section: Advanced Thermal Simulation Techniques In Sslbmentioning
confidence: 99%
“…The machine learning technology is a powerful toolkit holding the great promise for rapid structure prediction and mechanism investigation in electrolytes. Yao et al 164 employed neural network potential to simulate materials composed of Li, Zr/Hf, and Cl using stochastic surface walking method, and identified two potential unique layered halide SSEs, named Li 2 ZrCl 6 and Li 2 HfCl 6 , for stable SSEs (Figure 16C). The predicted halide SSEs possess high Li + conductivity and outstanding compatibility with Li metal anodes.…”
Section: Advanced Thermal Simulation Techniques In Sslbmentioning
confidence: 99%
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“…However, these occupied liquid organic electrolytes suffer from liquid leakage, flammability, and severe electrochemical decomposition in contact with Na metal and cathodes. In this regard, all-solid-state batteries (ASSBs) by utilizing inorganic solid-state electrolytes (SSEs) could maximize the energy densities of battery systems and greatly enhance battery safety as well. Therefore, advantages, such as Na ionic conductivity (∼10 –3 S cm –1 ) comparable to that of the liquid-state electrolytes at room temperature (RT), good structural stability, feasible ion activation energy, and wide electrochemical windows with the electrode materials, attracted considerable research interest in the exploration of sodium SSEs. …”
Section: Introductionmentioning
confidence: 99%