掺杂剂工程优化锂在Li7PS6银辉石中的离子电导率

IF 7 2区 材料科学 Q2 CHEMISTRY, PHYSICAL
Sokseiha Muy*, Thierry Le Mercier, Marion Dufour, Marc-David Braida, Antoine A. Emery and Nicola Marzari*, 
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引用次数: 0

摘要

含锂银柱石代表了一个很有前途的锂离子导体家族,其几种衍生化合物表现出室温离子电导率>;1 mS/cm,使其成为固态锂离子电池电解质的潜在候选者。从母相Li7PS6开始,已经尝试了几种阳离子和阴离子取代策略来增加Li离子的电导率。然而,对原生缺陷和锂银晶石掺杂的热力学及其对锂离子电导率的影响的详细了解尚不清楚。在这里,我们报告了一项全面的计算研究,研究了母相Li7PS6在内在和外在制度下的缺陷化学,使用一种新开发的工作流程,在热力学一致的框架下自动计算几种缺陷形成能量。我们的研究结果与已知的实验结果一致,排除了几种不利的共价掺杂剂,并缩小了可以通过实验测试的潜在有希望的候选物。我们还发现,正阴离子共掺杂可以为进一步优化银辉石的组成提供有力的策略。特别是硅-氟共掺杂被预测为热力学有利;这可能导致合成第一个含f掺杂锂的银辉石。最后,利用DeePMD神经网络,我们绘制了离子电导率景观图,作为从缺陷计算中确定的最有希望的阳离子和阴离子掺杂剂浓度的函数,并确定了成分空间中最有希望的高锂电导率区域,可以进行实验探索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Ionic Conductivity of Lithium in Li7PS6 Argyrodite via Dopant Engineering

Li-containing argyrodites represent a promising family of Li-ion conductors, with several derived compounds exhibiting room-temperature ionic conductivity >1 mS/cm, making them attractive as potential candidates for electrolytes in solid-state Li-ion batteries. Starting from the parent phase Li7PS6, several cation and anion substitution strategies have been attempted to increase the conductivity of the Li ions. Nonetheless, a detailed understanding of the thermodynamics of native defects and doping of Li argyrodite and their effect on the ionic conductivity of Li is missing. Here, we report a comprehensive computational study of the defect chemistry of the parent phase Li7PS6 in both intrinsic and extrinsic regimes, using a newly developed workflow to automate the computations of several defect formation energies in a thermodynamically consistent framework. Our findings agree with known experimental findings, rule out several unfavorable aliovalent dopants, and narrow down the potential promising candidates that can be tested experimentally. We also find that cation–anion codoping can provide a powerful strategy to further optimize the composition of argyrodite. In particular, Si–F codoping is predicted to be thermodynamically favorable; this could lead to the synthesis of the first F-doped Li-containing argyrodite. Finally, using DeePMD neural networks, we have mapped the ionic conductivity landscape as a function of the concentration of the most promising cation and anion dopants identified from the defect calculations, and identified the most promising region in the compositional space with high Li conductivity that can be explored experimentally.

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来源期刊
Chemistry of Materials
Chemistry of Materials 工程技术-材料科学:综合
CiteScore
14.10
自引率
5.80%
发文量
929
审稿时长
1.5 months
期刊介绍: The journal Chemistry of Materials focuses on publishing original research at the intersection of materials science and chemistry. The studies published in the journal involve chemistry as a prominent component and explore topics such as the design, synthesis, characterization, processing, understanding, and application of functional or potentially functional materials. The journal covers various areas of interest, including inorganic and organic solid-state chemistry, nanomaterials, biomaterials, thin films and polymers, and composite/hybrid materials. The journal particularly seeks papers that highlight the creation or development of innovative materials with novel optical, electrical, magnetic, catalytic, or mechanical properties. It is essential that manuscripts on these topics have a primary focus on the chemistry of materials and represent a significant advancement compared to prior research. Before external reviews are sought, submitted manuscripts undergo a review process by a minimum of two editors to ensure their appropriateness for the journal and the presence of sufficient evidence of a significant advance that will be of broad interest to the materials chemistry community.
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