Unveiling the Lithium-Ion Transport Mechanism in Li2ZrCl6 Solid-State Electrolyte via Deep Learning-Accelerated Molecular Dynamics Simulations
Hanzeng Guo, Volodymyr Koverga, Selva Chandrasekaran Selvaraj, Anh T. Ngo

TL;DR
This study uses deep learning-accelerated molecular dynamics simulations to uncover how lithium ions move in Li2ZrCl6 solid electrolytes, revealing phase-dependent diffusion mechanisms and structural factors influencing ionic conductivity.
Contribution
It introduces a deep learning-accelerated simulation approach to analyze lithium-ion transport mechanisms in Li2ZrCl6, providing atomic-scale insights for electrolyte design.
Findings
Disordered {1}-LZC shows highest ionic conductivity.
Lithium migration occurs via site-to-site hopping across phases.
ZrCl6 octahedral softening influences energy barriers and ion mobility.
Abstract
Lithium zirconium chlorides (LZCs) present a promising class of cost-effective solid electrolyte for next-generation all-solid-state batteries. The unique crystal structure of LZCs plays a crucial role in facilitating lithium-ion mobility, which further affects its electrochemical performance. To understand the underlying mechanism governing ion transport, we employed deep learning-accelerated molecular dynamics simulation on Li2ZrCl6 (trigonal {\alpha}- and monoclinic \b{eta}-LZC), focusing specifically on the zirconium coordination environment. Our results reveal that disordered {\alpha}-LZC exhibits the highest ionic conductivity, while \b{eta}-LZC demonstrates significantly lower conductivity, closely aligning with experimental findings. The study confirms that across all phases, lithium migration proceeds via site-to-site hopping mechanism, where variations in site residence times…
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