Molecular Dynamics Simulation of Li-Ion Conduction at Grain Boundaries in NASICON-Type LiZr2(PO4)3 Solid Electrolytes

Molecular Dynamics Simulation of Li-Ion Conduction at Grain Boundaries in NASICON-Type LiZr2(PO4)3 Solid Electrolytes
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DOI:
10.1021/acs.jpcc.1c07314
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发表时间:
2021-10
期刊:
The Journal of Physical Chemistry C
影响因子:
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通讯作者:
Koki Nakano;Naoto Tanibata;Hayami Takeda;R. Kobayashi;Masanobu Nakayama;Naoki Watanabe
Koki Nakano;Naoto Tanibata;Hayami Takeda;R. Kobayashi;Masanobu Nakayama;Naoki Watanabe
中科院分区:
其他
文献类型:
--
作者:
Koki Nakano;Naoto Tanibata;Hayami Takeda;R. Kobayashi;Masanobu Nakayama;Naoki Watanabe

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Na超离子导体型LiZr 2(PO 4)3(LZP)相关材料被认为是有前途的固体电解质,可以帮助实现具有高Li离子电导率和电化学稳定性的可再充电全固态Li离子电池。然而,晶界(GB)电阻显著降低了烧结多晶体的总Li离子电导率,这在LZP和几种其他Li离子导电氧化物中观察到。在这方面,固-固界面的合理设计是已知的,以提高离子电导率。因此,从实用性和阐明结晶固体动力学的基本知识的角度来看,研究GB的离子传导机制是重要的。在这项研究中,32 GB模型,包括各种米勒指数和终端,并相应的GB锂离子电导率进行了评估,使用分子动力学模拟与密度泛函理论推导的力场参数。与体离子电导率相比,一些GB模型表现出改善的锂离子电导率。使用来自界面结构特征的描述符的机器学习分析表明,原始Li 6 b位点周围的空腔大小显著影响GB离子电导率,这可以使GB结构的合理设计成为可能。
Na superionic conductor-type LiZr2(PO4)3(LZP)-related materials are considered promising solid electrolytes that can assist in realizing rechargeable all-solid-state Li-ion batteries with high Li-ion conductivity and electrochemical stability. However, the grain boundary (GB) resistance considerably reduces the total Li-ion conductivity of the sintered polycrystalline body, which is observed in LZP and several other Li-ion conductive oxides. In this regard, the rational design of solid–solid interfaces is known to improve the ionic conductivity. Therefore, examining the ion conduction mechanism at GBs is important from the viewpoints of practical usability and elucidation of the fundamental knowledge on dynamics in crystalline solids. In this study, 32 GB models were constructed, consisting of various Miller indices and terminations, and the corresponding GB Li-ion conductivities were evaluated using molecular dynamics simulations with density functional theory-derived force-field parameters. A few of the GB models exhibited improved Li-ion conductivities compared to the bulk ionic conductivity. Machine learning analysis using descriptors derived from interfacial structure characteristics suggested that the size of cavities around the original Li 6b sites significantly affected the GB ionic conductivity, which could enable the rational design of GB structures.