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arXiv 2401.05151cond-mat.mtrl-sci
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固态电解质中无序引起的锂离子传输增强

Disorder-induced enhancement of lithium-ion transport in solid-state electrolytes

Zhimin Chen, Tao Du, N. M. Anoop Krishnan, Yuanzheng Yue, Morten M. Smedskjaer

AI总结:

研究通过深度学习势模拟不同无序程度的Li3PS4电解质,发现无序驱动的扩散动力学显著增强室温电导率,并利用机器学习结构指纹揭示离子传导机制。

摘要:

提升固态电解质中的离子传导对于高性能全固态锂离子电池(LIBs)的发展至关重要。硫代磷酸锂是最具前景的固态电解质之一,因为它们在室温下表现出超离子导电性。然而,对其离子传导机制,尤其是结构无序对离子电导率的影响缺乏全面理解,是一个长期存在的问题,限制了全固态LIBs的进一步创新。在此,我们通过建立并采用深度学习势来模拟具有不同无序程度的Li3PS4电解质系统,以应对这一挑战。结果表明,无序驱动的扩散动力学显著增强了室温电导率。我们进一步通过应用一种称为“softness”的基于机器学习的结构指纹,在动力学特征、局部结构特征和原子重排之间建立了桥梁。该指标允许对无序诱导的“软”跳跃锂离子进行分类。我们的发现为复杂无序结构中的离子传导机制提供了见解,从而有助于为LIBs开发更优越的固态电解质。

英文摘要:

Enhancing the ion conduction in solid electrolytes is critically important for the development of high-performance all-solid-state lithium-ion batteries (LIBs). Lithium thiophosphates are among the most promising solid electrolytes, as they exhibit superionic conductivity at room temperature. However, the lack of comprehensive understanding regarding their ion conduction mechanism, especially the effect of structural disorder on ionic conductivity, is a long-standing problem that limits further innovations of all-solid-state LIBs. Here, we address this challenge by establishing and employing a deep learning potential to simulate Li3PS4 electrolyte systems with varying levels of disorder. The results show that disorder-driven diffusion dynamics significantly enhances the room-temperature conductivity. We further establish bridges between dynamical characteristics, local structural features, and atomic rearrangements by applying a machine learning-based structure fingerprint termed "softness". This metric allows the classification of the disorder-induced "soft" hopping lithium ions. Our findings offer insights into ion conduction mechanisms in complex disordered structures, thereby contributing to the development of superior solid-state electrolytes for LIBs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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