Abstract
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.
Highlights
All-solid-state batteries are the promising candidates for electric vehicles[1], smart grid[2] and other electrochemical power sources, due to their high safety, long cycle life and high energy density[3,4,5,6]
31,499 Crystallographic Information Files (CIFs)[73,74] for Li, Na, Mg and Al containing compositions were extracted from the Inorganic Crystal Structure Database (ICSD; release 2010/2)[75]
We find the interstices in paths Li3-Li1-Li3 and Li3-It11-Li3 are distributed at the valley of energy profile, and the bottlenecks coincide with the peak (Fig. 9)
Summary
All-solid-state batteries are the promising candidates for electric vehicles[1], smart grid[2] and other electrochemical power sources, due to their high safety, long cycle life and high energy density[3,4,5,6]. At the opposite side of the spectrum, the low-cost empirical methods, such as geometric analysis[11,12,13,14,15] and bond valence method (BV)[16,17,18,19,20,21], have gained popularity in high-throughput screening[22,23,24,25,26,27,28,29] and obtaining preliminary insights quickly for further accurate calculations In this context, we have developed a high-throughput screening platform[30] that integrates material database with hierarchical ion-transport calculations realized by implementing empirical algorithms to assist in FP-NEB calculation. To meet the requirements of our platform for automated unsupervised workflow, we independently developed the bond valence site energy (BVSE) calculation program (has been reported in our paper31) and the geometry-based ion-transport analysis library CAVD (presented in this paper)
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