Phase stability of Au-Li binary systems studied using neural network potential
Phase stability of Au-Li binary systems studied using neural network potential
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DOI：
10.1103/physrevb.103.094112
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发表时间：
2020-08
影响因子：
3.7
通讯作者：
K. Shimizu;E. Arguelles;Wenwen Li;Y. Ando;E. Minamitani;Satoshi Watanabe
中科院分区：
文献类型：
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作者：
K. Shimizu;E. Arguelles;Wenwen Li;Y. Ando;E. Minamitani;Satoshi Watanabe
Au和Li的相容性显示出作为二次电池中的粘附层和电极材料的潜在应用。在这里，探索合金化性能，我们构建了一个神经网络势（NNP）的Au-Li二元系统的密度泛函理论（DFT）计算的基础上。为了加速NNPs的构建，我们提出了一种高效且廉价的结构化数据集生成方法。所构造的NNP对晶格参数和声子性质的预测与DFT计算结果吻合得很好。我们还研究了Au$_{1-x}$Li$_{x}$的混合能，与DFT的验证结果吻合得很好。我们发现在凸船体上和略高于凸船体的地方存在各种结构的成分，这可以解释以往研究中对Au-Li稳定相缺乏共识的现象。此外，我们还新发现了Au$_{0.469}$Li$_{0.531}$作为稳定相，这在其他地方从未有过报道。最后，我们研究了从相分离结构到完全混合相的合金化过程。我们发现当多个相邻的Au原子溶解到Li中时，整个Au/Li界面的合金化从溶解区域开始。本文论证了NNPs对混溶相的适用性，并提供了合金化机制的理解。
The miscibility of Au and Li exhibits a potential application as an adhesion layer and electrode material in secondary batteries. Here, to explore alloying properties, we constructed a neural network potential (NNP) of Au-Li binary systems based on density functional theory (DFT) calculations. To accelerate construction of NNPs, we proposed an efficient and inexpensive method of structural dataset generation. The predictions by the constructed NNP on lattice parameters and phonon properties agree well with those obtained by DFT calculations. We also investigated the mixing energy of Au$_{1-x}$Li$_{x}$ with fine composition grids, showing excellent agreement with DFT verifications. We found the existence of various compositions with structures on and slightly above the convex hull, which can explain the lack of consensus on the Au-Li stable phases in previous studies. Moreover, we newly found Au$_{0.469}$Li$_{0.531}$ as a stable phase, which has never been reported elsewhere. Finally, we examined the alloying process starting from the phase separated structure to the complete mixing phase. We found that when multiple adjacent Au atoms dissolved into Li, the alloying of the entire Au/Li interface started from the dissolved region. This paper demonstrates the applicability of NNPs toward miscible phases and provides the understanding of the alloying mechanism.