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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2508.05598 |
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| _version_ | 1866909831290421248 |
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| author | Guo, Hanzeng Koverga, Volodymyr Selvaraj, Selva Chandrasekaran Ngo, Anh T. |
| author_facet | Guo, Hanzeng Koverga, Volodymyr Selvaraj, Selva Chandrasekaran Ngo, Anh T. |
| contents | 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 α- and monoclinic \b{eta}-LZC), focusing specifically on the zirconium coordination environment. Our results reveal that disordered α-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 critically impact the overall ionic conductivity. In α-LZCs, lithium ions prefer to anisotropically diffuse across interlayers as the result of lower energy barrier, driven primarily by collective diffusion. In contrast, lithium ions in \b{eta}-LZC primarily isotropically diffuse within intralayer, hindered by higher energy barriers and determined by individual diffusion. The variation in ZrCl62- octahedral unit softening, induced by the specific layered arrangement of zirconium atoms, emerges as a critical determinant of the energy barriers across the LZC phases. These atomic-scale insights into the transport processes provide valuable guidance for the rational design and optimization of LZCs-based electrolytes, accelerating their practical application in advanced energy storage technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05598 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unveiling the Lithium-Ion Transport Mechanism in Li2ZrCl6 Solid-State Electrolyte via Deep Learning-Accelerated Molecular Dynamics Simulations Guo, Hanzeng Koverga, Volodymyr Selvaraj, Selva Chandrasekaran Ngo, Anh T. Materials Science 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 α- and monoclinic \b{eta}-LZC), focusing specifically on the zirconium coordination environment. Our results reveal that disordered α-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 critically impact the overall ionic conductivity. In α-LZCs, lithium ions prefer to anisotropically diffuse across interlayers as the result of lower energy barrier, driven primarily by collective diffusion. In contrast, lithium ions in \b{eta}-LZC primarily isotropically diffuse within intralayer, hindered by higher energy barriers and determined by individual diffusion. The variation in ZrCl62- octahedral unit softening, induced by the specific layered arrangement of zirconium atoms, emerges as a critical determinant of the energy barriers across the LZC phases. These atomic-scale insights into the transport processes provide valuable guidance for the rational design and optimization of LZCs-based electrolytes, accelerating their practical application in advanced energy storage technologies. |
| title | Unveiling the Lithium-Ion Transport Mechanism in Li2ZrCl6 Solid-State Electrolyte via Deep Learning-Accelerated Molecular Dynamics Simulations |
| topic | Materials Science |
| url | https://arxiv.org/abs/2508.05598 |