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最近，一些通过ab-initio分子动力学(AIMD)模拟的计算研究表明，阴离子旋转运动和阳离子平移运动之间的耦合动力学会增强阳离子的扩散，称为桨轮效应。这些AIMD模拟主要在高温下进行，以加强离子扩散事件的采样，并在~100皮秒(ps) 的时间水平上获得充分的扩散统计，以避免低温AIMD模拟统计误差。此外，由阿伦尼乌斯假设所外推的室温离子电导率和扩散率通常较实验数据有很大误差。由于这些聚阴离子旋转和桨轮效应通常是在具有大自由体积和原子动能的高温AIMD模拟中观察到的，因此在低温条件下锂离子导体中是否存在阴离子旋转、是否存在桨轮效应仍然是一个问题。目前，ab-initio计算、分子动力学(MD)模拟和机器学习(ML)方法被广泛用于研究和开发先进电池材料。特别是ML结合经典计算方法，是高效预测性能和分析电池材料复杂结构-功能关系的有力工具。基于机器学习原子间势的分子动力学模拟，即所谓的机器学习分子动力学(MLMD)模拟，通过密度泛函理论(DFT)计算直接求解Schrödinger方程得到能量和力，是延长模拟时间的一种非常有力的工具。

来自南京航空航天大学郑明波教授团队、中科院深圳先进技术研究院赵海涛研究员团队、复旦大学夏永姚教授团队，合作为Li₇P₃S₁₁、Li₁₀GeP₂S₁₂、β-Li₃PS₄、Li₃ErCl₆、Li₃YBr₆等5种锂离子导体开发了机器学习原子间势模型，并开展基于机器学习原子间势的微秒级室温分子动力学模拟（MLMD），探索五种锂离子导体中的非阿伦尼乌斯行为、阴离子旋转事件、桨轮效应，为我们提供室温离子导体中阴离子旋转与阳离子扩散关系的直接物理图像。

Figure 1. Angle evolutions of P-S bonds.

作者研究发现Li₃ErCl₆的锂离子呈现非线性阿伦尼乌斯行为，这是传统AIMD模拟高估其离子电导率的主要原因。MLMD 模拟捕获室温Li₇P₃S₁₁中的阴离子旋转事件（图1），长链 [P₂S₇]⁴⁻中 [PS₄]³⁻ 四面体被发现具有旋转运动能力，而孤立的基团 [ PS₄]³⁻ 不旋转。然而， 室温下，Li₁₀GeP₂S₁₂、β-Li₃PS₄、Li₃ErCl₆和 Li₃YBr₆ 晶体中没有观察到聚阴离子旋转。此外，超长时间MLMD模拟表明室温条件下Li₇P₃S₁₁晶体不仅不存在桨轮效应，而且旋转的[PS₄]³⁻聚阴离子基团对整体Li离子扩散具有微弱的负面影响（图2）。

Figure 2. The relationships between [PS₄]³⁻polyanion groups (5#-P, 6#-P, 7#-P and 8#-P) and their adjacent Li ions during [PS₄]³⁻ rotation time for 300 K MLMD simulations of Li₇P₃S₁₁.

Machine learning molecular dynamics simulation identifying weakly negative effect of polyanion rotation on Li-ion migration

Zhenming Xu, Huiyu Duan, Zhi Dou, Mingbo Zheng, Yixi Lin, Yinghui Xia, Haitao Zhao & Yongyao Xia 

Understanding the physical picture of Li ion transport in the current ionic conductors is quite essential to further develop lithium superionic conductors for solid-state batteries. The traditional practice of directly extrapolating room temperature ion diffusion properties from the high-temperature (>600 K) ab initio molecular dynamics simulations (AIMD) simulations by the Arrhenius assumption unavoidably cause some deviations. Fortunately, the ultralong-time molecular dynamics simulation based on the machine-learning interatomic potentials (MLMD) is a more suitable tool to probe into ion diffusion events at low temperatures and simultaneously keeps the accuracy at the density functional theory level. Herein, by the low-temperature MLMD simulations, the non-linear Arrhenius behavior of Li ion was found for Li3ErCl6, which is the main reason for the traditional AIMD simulation overestimating its ionic conductivity. The 1μs MLMD simulations capture polyanion rotation events in Li7P3S11 at room temperature, in which four [PS4]3− tetrahedra belonging to a part of the longer-chain [P2S7]4− group are noticed with remarkable rotational motions, while the isolated group [PS4]3− does not rotate. However, no polyanion rotation is observed in Li10GeP2S12, β-Li3PS4, Li3ErCl6, and Li3YBr6 at 300 K during 1μs simulation time. Additionally, the ultralong-time MLMD simulations demonstrate that not only there is no paddle-wheel effect in the crystalline Li7P3S11 at room temperature, but also the rotational [PS4]3− polyanion groups have weakly negative impacts on the overall Li ion diffusion. The ultralong-time MLMD simulations deepen our understanding of the relationship between the polyanion rotation and cation diffusion in ionic conductors at room environments.