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).

中文翻译：

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通过机器学习模拟设计的卤化物电解质实现稳定的全固态锂金属电池

具有超离子电导率和界面稳定性的固体电解质 (SE) 是稳定的全固态锂金属电池 (ASSLMB) 的理想材料。在这里，我们使用神经网络势来模拟由 Li、Zr/Hf 和 Cl 组成的材料，使用随机表面行走方法，并识别出两种潜在的独特层状卤化物 SE，分别命名为 Li ₂ ZrCl ₆和 Li ₂ HfCl ₆，用于稳定的 ASSLMB。预测的卤化物 SEs 具有高 Li ⁺导电性和与锂金属负极的出色相容性。我们合成了这些 SE，并展示了它们对锂金属负极的优异稳定性，并以 4000 小时的稳定锂电镀/剥离性能创纪录。我们使用这些卤化物 SE 进一步制造了原型稳定的 ASSLMB，无需任何界面改性，显示出较小的内部阴极/SE 电阻 (19.48 Ω cm ² )、高平均库仑效率 (~99.48%)、良好的倍率性能 (63 mAh g ^-1 at 1.5 C）和前所未有的循环稳定性（在 0.5 C 下 70 个循环的容量保持率为 87%）。