Machine learning molecular dynamics simulations unraveling No paddle- wheel effect in Li2B12H12 solid-state electrolyte at room temperature

ZM Xu and YX Lin and YH Xia and YQ Jiang and XM Feng and ZH Liu and LF Shen and MB Zheng and YY Xia, JOURNAL OF POWER SOURCES, 637, 236591 (2025).

DOI: 10.1016/j.jpowsour.2025.236591

Understanding the fast ion migration mechanism in ionic conductors by the ab-initio molecular dynamics (AIMD) simulations at the atomic scale plays a crucial role in the further optimization and development of superionic conductors. Sometimes, the room temperature (RT) diffusion properties extrapolated from the elevated temperature AIMD simulation with short simulation time and limited supercell size by the linear Arrhenius assumption are problematic. The recently emerging machine learning interatomic potential models enabling the ultra-long time and ultra-large size molecular dynamics simulations and maintaining the accuracy of the density functional theory calculation have caused great attentions. Herein, we utilized the deep neural network model to develop the machine learning interatomic potential for Li2B12H12 and conducted the large-scale machine learning molecular dynamics (MLMD) simulations, investigating the effects of Li vacancy concentration and temperature on B12H122- anion group rotation. B12H122- anion groups display the limited vibrational motion rather than remarkable rotation in Li2B12H12 without any Li vacancy defect at RT. Moreover, our MLMD simulations demonstrate the absence of the "paddle-wheel" effect even in the Li vacancy-rich Li2B12H12 at RT. Oppositely, the rotational B12H122- anion groups have a negative impact on Li ion diffusion. These MLMD simulation results deepen our understandings of the relationship between anion rotation and cation diffusion in solids at RT.

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