通过神经网络辅助分子动力学模拟重温室温下固态电解质中锂离子的平面内和通道内扩散

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL Physical Chemistry Chemical Physics Pub Date : 2025-01-17 DOI:10.1039/D4CP04472J
Yao Huang, Dan Zhao, Mingsen Deng and Hujun Shen
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引用次数: 0

摘要

发展超离子导体(SIC)材料为在固态电解质中实现高离子电导率提供了一条有希望的途径。Li10GeP2S12 (LGPS)家族由于在各种SIC材料中具有优异的离子导电性而受到广泛关注。从头算分子动力学(AIMD)模拟已被广泛用于探索Li +离子在Li10GeP2S12中的扩散行为。这些模拟表明,Li +离子沿着一维(1D)链方向快速扩散,特别是沿着c轴扩散,这一过程被称为通道内扩散。此外,这些计算研究已经确定了ab平面内的额外扩散路径,称为面内扩散。然而,用于研究Li10GeP2S12室温下动态行为的AIMD模拟技术在时间尺度上仍然存在明显的局限性。在这项研究中,我们训练了LGPS系统的深电位(DP)模型,并进行了300纳秒的DeePMD模拟,以研究Li10GeP2S12在室温下的扩散行为。神经网络(NN)辅助模拟表明,LGPS的框架结构在整个300纳秒周期内保持显著稳定。接下来,我们的研究重点是在ab平面内的二维扩散路径(面内扩散机制)和沿c轴的一维扩散通道(通道内扩散机制)。通过分析DeePMD模拟结果,我们确定了ab平面内Li +扩散的两种不同途径:Li-2和Li-4途径。我们确定两种扩散路径的能垒分别为0.23 eV和0.34 eV,与最近的实验和理论结果定性一致。对于沿c轴的通道内扩散,我们计算的能量势垒约为0.083 eV,与之前的单粒子势(OPP)分析非常吻合。我们的研究结果证实了实验和理论研究,表明锂离子在通道内扩散时所经历的阻力明显小于平面内迁移机制。然而,我们的研究结果表明,尽管Li-2途径具有比Li-4途径更高的能量势垒,但Li-2途径仍然是平面内锂离子迁移的可行选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Revisiting the in-plane and in-channel diffusion of lithium ions in a solid-state electrolyte at room temperature through neural network-assisted molecular dynamics simulations†

Developing superionic conductor (SIC) materials offers a promising pathway to achieving high ionic conductivity in solid-state electrolytes (SSEs). The Li10GeP2S12 (LGPS) family has received significant attention due to its remarkable ionic conductivity among various SIC materials. Ab initio molecular dynamics (AIMD) simulations have been extensively used to explore the diffusion behavior of Li+ ions in Li10GeP2S12. These simulations indicate that Li+ ions diffuse rapidly along a one-dimensional (1D) chain direction, specifically along the c-axis, a process known as in-channel diffusion. In addition, these computational studies have identified additional diffusion pathways within the ab planes, referred to as in-plane diffusion. However, there are still notable limitations in the time scale associated with AIMD simulation techniques for studying the dynamic behavior of Li10GeP2S12 at room temperature. In this study, we trained a deep potential (DP) model for the LGPS system and performed a 300-nanosecond DeePMD simulation to investigate the diffusion behavior of Li10GeP2S12 at room temperature. The neural network (NN) assisted simulation showed that the framework structure of LGPS remained remarkably stable over the entire 300-nanosecond period. Following this, our investigation focused on the two-dimensional (2D) diffusion pathways within the ab plane (in-plane diffusion mechanism) and the 1D diffusion channel along the c-axis (in-channel diffusion mechanism). Upon analyzing the DeePMD simulation results, we identified two distinct pathways for in-plane Li+ diffusion within the ab plane: the Li-2 and Li-4 pathways. We determined the energy barriers for the two diffusion pathways to be 0.23 eV and 0.34 eV, respectively, in qualitative agreement with recent experimental and theoretical results. For the in-channel diffusion along the c-axis, our calculated energy barrier was approximately 0.083 eV, closely matching the previous one-particle potential (OPP) analysis. Our results confirm experimental and theoretical studies, indicating that lithium ions experience significantly less resistance when diffusing through the in-channel pathway compared to the in-plane migration mechanism. However, our findings suggest that despite having a higher energy barrier than the Li-4 pathway, the Li-2 pathway remains a viable option for in-plane lithium-ion migration.

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来源期刊
Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics 化学-物理:原子、分子和化学物理
CiteScore
5.50
自引率
9.10%
发文量
2675
审稿时长
2.0 months
期刊介绍: Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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