Chen Zhimin, Du Tao, Krishnan N M Anoop, Yue Yuanzheng, Smedskjaer Morten M
Department of Chemistry and Bioscience, Aalborg University, Aalborg East, Denmark.
Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
Nat Commun. 2025 Jan 26;16(1):1057. doi: 10.1038/s41467-025-56322-x.
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 of their ion conduction mechanism, especially the effect of structural disorder on ionic conductivity, is a long-standing problem that limits further innovations in all-solid-state LIBs. Here, we address this challenge by establishing and employing a deep learning potential to simulate LiPS 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.
提高固体电解质中的离子传导率对于高性能全固态锂离子电池（LIBs）的发展至关重要。硫代磷酸锂是最有前途的固体电解质之一，因为它们在室温下表现出超离子导电性。然而，对其离子传导机制缺乏全面的了解，尤其是结构无序对离子导电性的影响，是一个长期存在的问题，限制了全固态LIBs的进一步创新。在这里，我们通过建立和应用深度学习势来模拟具有不同无序程度的LiPS电解质系统，应对这一挑战。结果表明，无序驱动的扩散动力学显著提高了室温电导率。我们通过应用一种基于机器学习的结构指纹“柔软度”，进一步在动力学特征、局部结构特征和原子重排之间建立联系。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，从而有助于开发用于LIBs的优质固态电解质。
