Browsing by Author "Chi, ST"

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    CAVD, towards better characterization of void space for ionic transport analysis
    (Springer Nature, 2020-05-22) He, B; Ye, AJ; Chi, ST; Mi, PH; Ran, YB; Zhang, LW; Zou, XX; Pu, BW; Zhao, Q; Zou, Z; Wang, D; Zhang, WQ; Zhao, JT; Avdeev, M; Shi, S
    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. © 2020, The Author(s)
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    A database of ionic transport characteristics for over 29 000 inorganic compounds
    (Wiley, 2020-06-25) Zhang, LW; He, B; Zhao, Q; Zou, ZY; Chi, ST; Mi, PH; Ye, AJ; Li, YJ; Wang, D; Avdeev, M; Adams, S; Shi, S
    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.
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    High-throughput screening platform for solid electrolytes combining hierarchical ion-transport prediction algorithms
    (Springer Nature, 2020-05-21) He, B; Chi, ST; Ye, AJ; Mi, PH; Zhang, LW; Pu, B; Zou, Z; Ran, YB; Zhao, Q; Wang, D; Zhang, WQ; Zhao, JT; Adams, S; Avdeev, M; Shi, S
    The combination of a materials database with high-throughput ion-transport calculations is an effective approach to screen for promising solid electrolytes. However, automating the complicated preprocessing involved in currently widely used ion-transport characterization algorithms, such as the first-principles nudged elastic band (FP-NEB) method, remains challenging. Here, we report on high-throughput screening platform for solid electrolytes (SPSE) that integrates a materials database with hierarchical ion-transport calculations realized by implementing empirical algorithms to assist in FP-NEB completing automatic calculation. We first preliminarily screen candidates and determine the approximate ion-transport paths using empirical both geometric analysis and the bond valence site energy method. A chain of images are then automatically generated along these paths for accurate FP-NEB calculation. In addition, an open web interface is actualized to enable access to the SPSE database, thereby facilitating machine learning. This interactive platform provides a workflow toward high-throughput screening for future discovery and design of promising solid electrolytes and the SPSE database is based on the FAIR principles for the benefit of the broad research community. © 2020, The Author(s)
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    A highly efficient and informative method to identify ion transport networks in fast ion conductors
    (Elsevier, 2021-01-15) He, B; Mi, PH; Ye, AJ; Chi, ST; Jiao, Y; Zhang, LW; Pu, BW; Zou, Z; Zhang, WQ; Avdeev, M; Adams, S; Zhao, JT; Shi, S
    High-throughput analysis of the ion transport pathways is critical for screening fast ion conductors. Currently, empirical methods, such as the geometric analysis and bond valence site energy (BVSE) methods, are respectively used for the task. Geometric analysis method can only extract geometric and topological pathway properties without considering the interatomic interactions, while the BVSE method alone does not yield a geometric classification of the sites and interstices forming the pathway. Herein, we propose a highly efficient and informative method to identify interstices and connecting segments constructing an ion transport network by combining topological pathway network and BVSE landscape, which enables to obtain both the geometry and energy profiles of nonequivalent ion transport pathways between adjacent lattice sites. These pathways can be further used as the input for first-principles nudged elastic band calculations with automatically generated chains of images. By performing high-throughput screening of 48,321 Li-, Na-, Mg- and Al-containing ionic compounds from the Inorganic Crystal Structure Database based on the filter combining geometric analysis and BVSE methods, we obtain 1,270 compounds with connected ionic migration pathways of suitable sizes and low migration energy barriers, which include both previously reported fast ion conductors, and new promising materials to be explored further. © 2020 Acta Materialia Inc. Published by Elsevier Ltd.