Please use this identifier to cite or link to this item: https://apo.ansto.gov.au/dspace/handle/10238/11057
Title: A database of ionic transport characteristics for over 29 000 inorganic compounds
Authors: Zhang, LW
He, B
Zhao, Q
Zou, ZY
Chi, ST
Mi, PH
Ye, AJ
Li, YJ
Wang, D
Avdeev, M
Adams, S
Shi, S
Keywords: Crystal structure
Inorganic compounds
Ionic conductivity
Machine learning
Lithium
Sodium
Potassium
Silver
Copper
Magnesium ions
Zinc ions
Calcium ions
Aluminium ions
Fluorine
Oxygen
Issue Date: 25-Jun-2020
Publisher: Wiley
Citation: Zhang, L., He, B., Zhao, Q., Zou, Z., Chi, S., Mi, P., Ye, A., Li, Y., Wang, D., Avdeev, M., Adams, S., & Shi, S. (2020). A database of ionic transport characteristics for over 29 000 inorganic compounds. Advanced Functional Materials, 30(35), 2003087. doi:10.1002/adfm.202003087
Abstract: Transport characteristics of ionic conductors play a key role in the performance of electrochemical devices such as solid-state batteries, solid-oxide fuel cells, and sensors. Despite the significance of the transport characteristics, they have been experimentally measured only for a very small fraction of all inorganic compounds, which limits the technological progress. To address this deficiency, a database containing crystal structure information, ion migration channel connectivity information, and 3D channel maps for over 29 000 inorganic compounds is presented. The database currently contains ionic transport characteristics for all potential cation and anion conductors, including Li+, Na+, K+, Ag+, Cu(2)+, Mg2+, Zn2+, Ca2+, Al3+, F−, and O2−, and this number is growing steadily. The methods used to characterize materials in the database are a combination of structure geometric analysis based on Voronoi decomposition and bond valence site energy (BVSE) calculations, which yield interstitial sites, transport channels, and BVSE activation energy. The computational details are illustrated on several typical compounds. This database is created to accelerate the screening of fast ionic conductors and to accumulate descriptors for machine learning, providing a foundation for large-scale research on ion migration in inorganic materials.© 1999-2021 John Wiley & Sons, Inc.
URI: https://doi.org/10.1002/adfm.202003087
https://apo.ansto.gov.au/dspace/handle/10238/11057
ISSN: 1616-3028
Appears in Collections:Journal Articles

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