He Bing, Ye Anjiang, Chi Shuting, Mi Penghui, Ran Yunbing, Zhang Liwen, Zou Xinxin, Pu Bowei, Zhao Qian, Zou Zheyi, Wang Da, Zhang Wenqing, Zhao Jingtai, Avdeev Maxim, Shi Siqi
School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China.
Sci Data. 2020 May 22;7(1):153. doi: 10.1038/s41597-020-0491-x.
Geometric crystal structure analysis using three-dimensional Voronoi tessellation provides intuitive insights into the ionic transport behavior of metal-ion electrode materials or solid electrolytes by mapping the void space in a framework onto a network. The existing tools typically consider only the local voids by mapping them with Voronoi polyhedra vertices and then define the mobile ions pathways using the Voronoi edges connecting these vertices. We show that in some structures mobile ions are located on Voronoi polyhedra faces and thus cannot be located by a standard approach. To address this deficiency, we extend the method to include Voronoi faces in the constructed network. This method has been implemented in the CAVD python package. Its effectiveness is demonstrated by 99% recovery rate for the lattice sites of mobile ions in 6,955 Li-, Na-, Mg- and Al-containing ionic compounds extracted from the Inorganic Crystal Structure Database. In addition, various quantitative descriptors of the network can be used to identify and rank the materials and further used in materials databases for machine learning.
使用三维Voronoi镶嵌的几何晶体结构分析通过将框架中的空隙空间映射到网络上，为金属离子电极材料或固体电解质的离子传输行为提供了直观的见解。现有工具通常仅通过用Voronoi多面体顶点映射局部空隙，然后使用连接这些顶点的Voronoi边来定义移动离子的路径。我们表明，在某些结构中，移动离子位于Voronoi多面体面上，因此无法通过标准方法定位。为了解决这一缺陷，我们将该方法扩展到在构建的网络中包含Voronoi面。该方法已在CAVD Python包中实现。从无机晶体结构数据库中提取的6955种含锂、钠、镁和铝的离子化合物中，移动离子晶格位点的回收率达到99%，证明了该方法的有效性。此外，网络的各种定量描述符可用于识别材料并对其进行排名，并进一步用于机器学习的材料数据库中。
