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arXiv 2401.11244cond-mat.mtrl-sci
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基于机器学习分子动力学精确描述固态离子导体中的离子迁移

Accurate Description of Ion Migration in Solid-State Ion Conductors from Machine-Learning Molecular Dynamics

Takeru Miyagawa, Namita Krishnan, Manuel Grumet, Christian Reverón Baecker, Waldemar Kaiser, David A. Egger

AI总结:

本文评估机器学习力场驱动的分子动力学对三类复杂固态离子导体离子迁移机制的预测能力,证明其以较低成本达到接近从头算模拟的精度。

摘要:

固态离子导体(SSICs)已成为电化学储能器件中一类很有前景的材料,并且此类新型化合物正不断被发现。能够从大量候选SSIC化合物中进行快速筛选的高通量方法,在这一探索过程中至关重要。尽管第一性原理方法在此背景下常被用于提供离子迁移机制的原子级细节,但这类动力学计算计算成本高昂,并限制了模拟中可触及的时间和长度尺度。在此,我们探索了近期开发的机器学习力场在预测SSICs中不同离子迁移机制方面的潜力。具体而言,我们系统研究了三类均表现出包括振动非谐性在内的复杂离子动力学的SSICs:AgI,一种强无序Ag$^+$导体;Na$_3$SbS$_4$,一种Na$^+$空位导体;以及Li$_{10}$GeP$_2$S$_{12}$,其具有协同Li$^+$迁移特征。通过与从头算分子动力学数据进行系统比较,我们证明机器学习分子动力学能够对结构和振动性质(包括这些SSICs中的复杂非谐动力学)给出非常准确的预测。机器学习分子动力学模拟以相对较低的计算成本实现从头算精度,为快速设计新型SSICs开辟了一条有前景的路径。

英文摘要:

Solid-state ion conductors (SSICs) have emerged as a promising material class for electrochemical storage devices and novel compounds of this kind are continuously being discovered. High-throughout approaches that enable a rapid screening among the plethora of candidate SSIC compounds have been essential in this quest. While first-principles methods are routinely exploited in this context to provide atomic-level details on ion migration mechanisms, dynamic calculations of this type are computationally expensive and limit us in the time- and length-scales accessible during the simulations. Here, we explore the potential of recently developed machine-learning force fields for predicting different ion migration mechanisms in SSICs. Specifically, we systematically investigate three classes of SSICs that all exhibit complex ion dynamics including vibrational anharmonicities: AgI, a strongly disordered Ag$^+$ conductor; Na$_3$SbS$_4$, a Na$^+$ vacancy conductor; and Li$_{10}$GeP$_2$S$_{12}$, which features concerted Li$^+$ migration. Through systematic comparison with \textit{ab initio} molecular dynamics data, we demonstrate that machine-learning molecular dynamics provides very accurate predictions of the structural and vibrational properties including the complex anharmonic dynamics in these SSICs. The \textit{ab initio} accuracy of machine-learning molecular dynamics simulations at relatively low computational cost open a promising path toward the rapid design of novel SSICs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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