CAVD, towards better characterization of void space for ionic transport analysis

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dc.contributor.authorHe, Ben_AU
dc.contributor.authorYe, AJen_AU
dc.contributor.authorChi, STen_AU
dc.contributor.authorMi, PHen_AU
dc.contributor.authorRan, YBen_AU
dc.contributor.authorZhang, LWen_AU
dc.contributor.authorZou, XXen_AU
dc.contributor.authorPu, BWen_AU
dc.contributor.authorZhao, Qen_AU
dc.contributor.authorZou, Zen_AU
dc.contributor.authorWang, Den_AU
dc.contributor.authorZhang, WQen_AU
dc.contributor.authorZhao, JTen_AU
dc.contributor.authorAvdeev, Men_AU
dc.contributor.authorShi, Sen_AU
dc.date.accessioned2021-06-29T21:00:31Zen_AU
dc.date.available2021-06-29T21:00:31Zen_AU
dc.date.issued2020-05-22en_AU
dc.date.statistics2021-06-28en_AU
dc.description.abstractGeometric 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. © 2020, The Author(s)en_AU
dc.identifier.articlenumber153en_AU
dc.identifier.citationHe, B., Ye, A., Chi, S., Mi, P., Ran, Y., Zhang, L., Zou, X., Pu, B., Zhao, Q., Zou, Z., Wang, D., Zhang, W., Zhao, J., Avdeev, M., & Shi, S. (2020). CAVD, towards better characterization of void space for ionic transport analysis. Scientific Data, 7, 153. doi:10.1038/s41597-020-0491-xen_AU
dc.identifier.issn2052-4463en_AU
dc.identifier.journaltitleScientific Dataen_AU
dc.identifier.pagination1-13en_AU
dc.identifier.urihttps://doi.org/10.1038/s41597-020-0491-xen_AU
dc.identifier.urihttps://apo.ansto.gov.au/dspace/handle/10238/10953en_AU
dc.identifier.volume7en_AU
dc.language.isoenen_AU
dc.publisherSpringer Natureen_AU
dc.subjectRadioisotope batteriesen_AU
dc.subjectElectrolytesen_AU
dc.subjectElectrodesen_AU
dc.subjectCrystal structureen_AU
dc.subjectElectrochemistryen_AU
dc.subjectIonic conductivityen_AU
dc.titleCAVD, towards better characterization of void space for ionic transport analysisen_AU
dc.typeJournal Articleen_AU
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