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arXiv 2403.20138cond-mat.mtrl-sci
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基于机器学习力场研究W掺杂Na₃SbS₄中Na空位驱动的相变与快离子传导

Na Vacancy Driven Phase Transformation and Fast Ion Conduction in W-doped Na$_3$SbS$_4$ from Machine Learning Force Fields

Johan Klarbring, Aron Walsh

AI总结:

本文通过等变图神经网络机器学习力场研究W掺杂Na₃SbS₄,揭示Na空位增加可在低温稳定立方相并增强Na离子扩散,而W掺杂剂直接作用有限,为超离子导体设计提供微观机制依据。

摘要:

固态钠电池需要能在室温下有效导电的电解质。Na₃SbS₄(Pn = P, Sb; Ch = S, Se)家族因其高Na离子电导率而受到研究。Na空位在这些材料中介导离子扩散,其数量可以通过在磷族元素位点进行异价掺杂来增加。为了探究外部掺杂的微观作用及其对扩散和相稳定性的影响,我们基于等变图神经网络为Na₃₋ₓWₓSb₁₋ₓS₄训练了一个机器学习力场。对大规模分子动力学轨迹的分析表明,Na空位数量的增加在较低温度下稳定了全局立方相,并增强了Na离子扩散,而替代式W掺杂剂的直接作用有限。在全局立方相中,我们观察到原子相对于平均对称性存在大尺度且长寿命的偏离,这与近期的实验建议相呼应。我们还提出了Na离子相关扩散的证据,这支撑了这些材料所被认为的超离子性质。

英文摘要:

Solid-state sodium batteries require effective electrolytes that conduct at room temperature. The Na$_3$SbS$_4$ (Pn = P, Sb; Ch = S, Se) family have been studied for their high Na ion conductivity. The population of Na vacancies, which mediate ion diffusion in these materials, can be enhanced through aliovalent doping on the pnictogen site. To probe the microscopic role of extrinsic doping, and its impact on diffusion and phase stability, we trained a machine learning force field for Na$_{3-x}$W$_{x}$Sb$_{1-x}$S$_4$ based on an equivariant graph neural network. Analysis of large-scale molecular dynamics trajectories shows that an increased Na vacancy population stabilises the global cubic phase at lower temperatures with enhanced Na ion diffusion, and that the explicit role of the substitutional W dopants is limited. In the global cubic phase we observe large and long-lived deviations of atoms from the averaged symmetry, echoing recent experimental suggestions. Evidence of correlated Na ion diffusion is also presented that underpins the suggested superionic nature of these materials.

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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