Holland Julian, Demeyere Tom, Bhandari Arihant, Hanke Felix, Milman Victor, Skylaris Chris-Kriton
School of Chemistry, University of Southampton, Southampton SO17 1BJ, U.K.
The Faraday Institution, Quad One, Becquerel Avenue, Harwell Campus, Didcot OX11, U.K.
J Phys Chem Lett. 2023 Nov 8;14(45):10257-10262. doi: 10.1021/acs.jpclett.3c02064.
To date, experimental and theoretical works have been unable to uncover the ground-state configuration of the solid electrolyte cubic LiLaZrO (-LLZO). Computational studies rely on an initial low-energy structure as a reference point. Here, we present a methodology for identifying energetically favorable configurations of -LLZO for a crystallographically predicted structure. We begin by eliminating structures that involve overlapping Li atoms based on nearest neighbor counts. We further reduce the configuration space by eliminating symmetry images from all remaining structures. Then, we perform a machine learning-based energetic ordering of all remaining structures. By considering the geometrical constraints that emerge from this methodology, we determine that a large portion of previously reported structures may not be feasible or stable. The method developed here could be extended to other ion conductors. We provide a database containing all of the generated structures with the aim of improving accuracy and reproducibility in future -LLZO research.
迄今为止，实验和理论研究都未能揭示固体电解质立方相LiLaZrO（-LLZO）的基态构型。计算研究依赖于一个初始的低能量结构作为参考点。在此，我们提出一种方法，用于识别针对晶体学预测结构的-LLZO能量上有利的构型。我们首先根据最近邻原子数消除涉及锂原子重叠的结构。通过从所有剩余结构中消除对称图像，我们进一步缩小了构型空间。然后，我们对所有剩余结构进行基于机器学习的能量排序。通过考虑该方法中出现的几何约束，我们确定先前报道的大部分结构可能不可行或不稳定。这里开发的方法可以扩展到其他离子导体。我们提供了一个包含所有生成结构的数据库，旨在提高未来-LLZO研究的准确性和可重复性。
