Browsing by Author "Zhao, Q"

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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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    Identifying descriptors for Li+ conduction in cubic Li-argyrodites via hierarchically encoding crystal structure and inferring causality
    (Elsevier, 2021-09-01) Zhao, Q; Zhang, LW; He, B; Ye, AJ; Avdeev, M; Chen, LQ; Shi, S
    Identifying descriptors linked to Li+ conduction enables rational design of solid state electrolytes (SSEs) for advanced lithium ion batteries, but it is hindered by the diverse and confounding descriptors. To address this, by integrating global and local effects of Li+ conduction environment, we develop a generic method of hierarchically encoding crystal structure (HECS) and inferring causality to identify descriptors for Li+ conduction in SSEs. Taking the cubic Li-argyrodites as an example, 32 HECS-descriptors are constructed, encompassing composition, structure, conduction pathway, ion distribution, and special ions derived from the unit cell information. Partial correlation analysis reveals that the smaller anion size plays a significant role in achieving lower activation energy, which results from the competing effects between the lattice space and bottleneck size controlled by framework site disorder. Moreover, the promising candidates are suggested, in which Li6-xPS5-xCl1+x (e.g., Li5.5PS4.5Cl1.5 with the room ionic conductivity of 9.4mS cm−1 and the activation energy of 0.29eV) have been experimentally evaluated as excellent candidates for practical SSEs and the rest are novel compositions waiting for validation. Our work establishes a rational correlation between the HECS-descriptors and Li+ conduction and the proposed approach can be extended to other types of SSE materials. © 2021 Elsevier B.V.
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    Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors
    (Elsevier, 2021-07-30) Zhao, Q; Avdeev, M; Chen, L; Shi, S
    Rational design of solid-state electrolytes (SSEs) with high ionic conductivity and low activation energy (Ea) is vital for all solid-state batteries. Machine learning (ML) techniques have recently been successful in predicting Li+ conduction property in SSEs with various descriptors and accelerating the development of SSEs. In this work, we extend the previous efforts and introduce a framework of ML prediction for Ea in SSEs with hierarchically encoding crystal structure-based (HECS) descriptors. Taking cubic Li-argyrodites as an example, an Ea prediction model is developed to the coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.887 and 0.02 eV for training dataset, and 0.820 and 0.02 eV for test dataset, respectively by partial least squares (PLS) analysis, proving the prediction power of HECS-descriptors. The variable importance in projection (VIP) scores demonstrate the combined effects of the global and local Li+ conduction environments, especially the anion size and the resultant structural changes associated with anion site disorder. The developed Ea prediction model directs us to optimize and design new Li-argyrodites with lower Ea, such as Li6–xPS5–xCl1+x (<0.322 eV), Li6+xPS5+xBr1–x (<0.273 eV), Li6+xPS5+xBr0.25I0.75–x (<0.352 eV), Li6+(5–n)yP1–yNyS5I (<0.420 eV), Li6+(5–n)yAs1–yNyS5I (<0.371 eV), Li6+(5–n)yAs1–yNySe5I (<0.450 eV), by broadening bottleneck size, invoking site disorder and activating concerted Li+ conduction. This analysis shows great potential in promoting rational design of advanced SSEs and the same approach can be applied to other types of materials.© 2021 Published by Elsevier B.V. on behalf of Science China Press.