ANSTO :: Browsing by Author "Shi, S"

Browsing by Author "Shi, S"

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Ab initio thermodynamic optimization of Ni-rich Ni–Co–Mn oxide cathode coatings
(Elsevier, 2020-02-29) Liu, B; Liu, JH; Yang, J; Wang, D; Ye, CC; Wang, DY; Avdeev, M; Shi, S; Yang, JH; Zhang, WQ
The effectiveness of surface coatings in improving the stability and cycling performance of cathodes has been demonstrated since they are first proposed in the 1990's. However, the progress since then is made mostly using the trial-and-error method. Herein, an automated electrochemical-chemical stability design scheme based on first-principles thermodynamics calculations of reaction models is presented to optimize coatings for Ni-rich nickel–cobalt–manganese oxide (NCM) cathodes. Given that the coating must possess a wider electrochemical window than the cathode without the occurrence of Li-ion redistribution at the cathode/coating interface, the reaction energies of both lithium insertion/extraction and decomposition process associated with the coating are used as one of the two screening criteria. As the coating is also required to be chemically stable in Li residues and hydrofluoric-acid containing liquid environment, the positive reaction energy achieved by adjusting molar ratio of the components is used as another criterion. Using these two screening criteria, we demonstrate that lithium-containing metal phosphates, rather than previously suggested Li-containing metal oxides, are the optimal coatings for Ni-rich NCM cathodes, which is confirmed experimentally. The proposed approach is general and can be used to find optimal coating materials for any other cathodes. © 2020 Elsevier B.V.
Auto-MatRegressor: liberating machine learning alchemists
(Elsevier, 2023-06-30) Liu, Y; Wang, S; Yang, Z; Avdeev, M; Shi, S
Machine learning (ML) is widely used to uncover structure–property relationships of materials due to its ability to quickly find potential data patterns and make accurate predictions. However, like alchemists, materials scientists are plagued by time-consuming and labor-intensive experiments to build high-accuracy ML models. Here, we propose an automatic modeling method based on meta-learning for materials property prediction named Auto-MatRegressor, which automates algorithm selection and hyperparameter optimization by learning from previous modeling experience, i.e., meta-data on historical datasets. The meta-data used in this work consists of 27 meta-features that characterize the datasets and the prediction performances of 18 algorithms commonly used in materials science. To recommend optimal algorithms, a collaborative meta-learning method embedded with domain knowledge quantified by a materials categories tree is designed. Experiments on 60 datasets show that compared with the traditional modeling method from scratch, Auto-MatRegressor automatically selects appropriate algorithms at lower computational cost, which accelerates constructing ML models with good prediction accuracy. Auto-MatRegressor supports dynamic expansion of meta-data with the increase of the number of materials datasets and other required algorithms and can be applied to any ML materials discovery and design task. © 2023 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.
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)
Correlated migration invokes higher Na+‐ion conductivity in NaSICON‐type solid electrolytes
(Wiley, 2019-10-01) Zhang, ZZ; Zou, Z; Kaup, K; Xiao, RJ; Shi, S; Avdeev, M; Hu, YS; Wang, D; He, B; Li, H; Huang, XY; Nazar, LF; Chen, LQ
Na super ion conductor (NaSICON), Na1+nZr2SinP3–nO12 is considered one of the most promising solid electrolytes; however, the underlying mechanism governing ion transport is still not fully understood. Here, the existence of a previously unreported Na5 site in monoclinic Na3Zr2Si2PO12 is unveiled. It is revealed that Na+‐ions tend to migrate in a correlated mechanism, as suggested by a much lower energy barrier compared to the single‐ion migration barrier. Furthermore, computational work uncovers the origin of the improved conductivity in the NaSICON structure, that is, the enhanced correlated migration induced by increasing the Na+‐ion concentration. Systematic impedance studies on doped NaSICON materials bolster this finding. Significant improvements in both the bulk and total ion conductivity (e.g., σbulk = 4.0 mS cm−1, σtotal = 2.4 mS cm−1 at 25 °C) are achieved by increasing the Na content from 3.0 to 3.30–3.55 mol formula unit−1. These improvements stem from the enhanced correlated migration invoked by the increased Coulombic repulsions when more Na+‐ions populate the structure rather than solely from the increased mobile ion carrier concentration. The studies also verify a strategy to enhance ion conductivity, namely, pushing the cations into high energy sites to therefore lower the energy barrier for cation migration. © 2019 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim
Data quantity governance for machine learning in materials science
(Oxford University Press (OUP), 2023-05-31) Liu, Y; Yang, ZW; Zou, XX; Ma, S; Liu, D; Avdeev, M; Shi, S
Data-driven machine learning (ML) is widely employed in the analysis of materials structure–activity relationships, performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction. However, because of the laborious process of materials data acquisition, ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size (for traditional ML models) or the mismatch between model parameters and sample size (for deep-learning models), usually resulting in terrible performance. Here, we review the efforts for tackling this issue via feature reduction, sample augmentation and specific ML approaches, and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity governance. Following this, we propose a synergistic data quantity governance flow with the incorporation of materials domain knowledge. After summarizing the approaches to incorporating materials domain knowledge into the process of ML, we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and applications. The work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML. © The Author(s) 2023. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Efficient potential-tuning strategy through p-type doping for designing cathodes with ultrahigh energy density
(Oxford Academic, 2020-07-27) Wang, ZQ; Wang, D; Zou, Z; Song, T; Ni, DX; Li, ZZ; Shao, XC; Yin, WJ; Wang, YC; Luo, WW; Wu, MS; Avdeev, M; Xu, B; Shi, S; Ouyang, CY; Chen, LQ
Designing new cathodes with high capacity and moderate potential is the key to breaking the energy density ceiling imposed by current intercalation chemistry on rechargeable batteries. The carbonaceous materials provide high capacities but their low potentials limit their application to anodes. Here, we show that Fermi level tuning by p-type doping can be an effective way of dramatically raising electrode potential. We demonstrate that Li(Na)BCF2/Li(Na)B2C2F2 exhibit such change in Fermi level, enabling them to accommodate Li+(Na+) with capacities of 290–400 (250–320) mAh g−1 at potentials of 3.4–3.7 (2.7–2.9) V, delivering ultrahigh energy densities of 1000–1500 Wh kg−1. This work presents a new strategy in tuning electrode potential through electronic band structure engineering. © The Author(s) 2020. Creative Commons CC BY Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.
FFMDFPA: a FAIRification framework for materials data with no-code flexible semi-structured parser and application programming interfaces
(American Chemical Society, 2023-08-28) He, B; Gong, Z; Avdeev, M; Shi, S
The FAIR Data Principles are guidelines to ensure Findability, Accessibility, Interoperability, and Reusability of digital resources, which are essential to accelerate data-driven materials science. Despite the development and growing adoption of the FAIR principles, appropriate implementation solutions and software to make data FAIR are still sparse, particularly in standardization of heterogeneous data and subsequent data access. Here, we introduce a FAIRification Framework for Materials Data with No-Code Flexible Semi-Structured Parser and API (FFMDFPA) (API, application programming interface) for raw data processing. Using a template-based parser, FFMDFPA can extract and transform semistructured data in various text formats, providing the flexibility to extend data manipulation without coding. Additionally, FFMDFPA provides a standardized API with efficient query syntax that facilitates seamless data sharing. Taking various text files generated by computational software as examples, we demonstrate the potential utility of FFMDFPA. This work offers important insights toward efficient utilization and reuse of materials data, and the data semantic manipulation implemented in the parser and API can be extended to textual data, which has implications for future data FAIRification. © American Chemical Society
High-throughput computational screening of Li-containing fluorides for battery cathode coatings
(American Chemical Society, 2020-12-16) Liu, B; Wang, D; Avdeev, M; Shi, S; Yang, J; Zhang, WQ
Cathode degradation is a key factor that limits the cycling stability and rate capability of Li-ion batteries. Coating the surface of cathode particles with metal oxides or fluorides has been reported to suppress this degradation. However, poor Li-ion conductivity of metal oxide and fluoride coatings typically decreases the overall ionic conductivity. In addition, side (electro)chemical reactions at the coating/cathode interface and coating/hydrofluoric acid liquid environment also limit the performance of Li-ion batteries. Identification of stable coating materials with high Li-ion conductivity, which is typically done via a trial-and-error approach, remains a challenge. In this work, we perform high-throughput computational screening of ternary Li-containing fluorides for application as cathode coatings for Li-ion batteries, focusing on their phase stability, electrochemical stability, chemical stability, and Li-ion conductivity. Using the tiered screening approach, we identify 10 promising coating candidates from all the 920 Li-containing fluorides listed in the Materials Project database, including the two experimentally studied Li2ZrF6 and Li2TiF6 compounds. The identified cathode coatings are expected to exhibit optimal battery cycling and rate performance. In particular, Li2MF6 (M = Si, Ge, Zr, Ti) compounds offer the best combination of electrochemical and chemical stability and ionic conductivity, surpassing the performance of common coatings such as oxides and binary fluorides. © 2019 American Chemical Society
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)
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.
Identifying chemical factors affecting reaction kinetics in Li-air battery via ab initio calculations and machine learning
(Elsevier, 2021-03-01) Wang, AP; Zou, ZY; Wang, D; Liu, Y; Li, YJ; Wu, JM; Avdeev, M; Shi, S
Redox mediators are promised to thermodynamically resolve the cathode irreversibility of Li-air battery. However, the sluggish chemical reaction between mediators and discharge products severely restrains fast charging. Here, we combine ab initio calculations and machine learning method to investigate the reaction kinetics between LiOH and I2, and demonstrate the critical role of the disorder degree of LiOH and the solvent effect. The Li+ desorption is identified as the rate determining step (rds) of the reaction. While LiOH turns from the crystalline to disordered/amorphous structure, the rds energy barrier will be reduced by ∼500 meV. The functional group of the solvent is detected as the key to regulating the solvation effect and phosphate-based solvent is predicted to accelerate the decomposition kinetics most with the strongest solvation capability. These findings indicate that the faster reaction kinetics between mediators and the discharge products can be achieved by rational discharge product structure regulation and appropriate solvent selection. © 2020 Elsevier B.V.
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.
Identifying migration channels and bottlenecks in monoclinic NASICON-type solid electrolytes with hierarchical ion-transport algorithms
(Wiley, 2021-09-07) Zou, Z; Ma, N; Wang, AP; Ran, YB; Song, T; He, B; Ye, AJ; Mi, PH; Zhang, LW; Zhou, H; Jiao, Y; Liu, JP; Wang, D; Li, YJ; Avdeev, M; Shi, S
Monoclinic natrium superionic conductors (NASICON; Na3Zr2Si2PO12) are well-known Na-ion solid electrolytes which have been studied for 40 years. However, due to the low symmetry of the crystal structure, identifying the migration channels of monoclinic NASICON accurately still remains unsolved. Here, a cross-verified study of Na+ diffusion pathways in monoclinic NASICON by integrating geometric analysis of channels and bottlenecks, bond-valence energy landscapes analysis, and ab initio molecular dynamics simulations is presented. The diffusion limiting bottlenecks, the anisotropy of conductivity, and the time and temperature dependence of Na+ distribution over the channels are characterized and strategies for improving both bulk and total conductivity of monoclinic NASICON-type solid electrolytes are proposed. This set of hierarchical ion-transport algorithms not only shows the efficiency and practicality in revealing the ion transport behavior in monoclinic NASICON-type materials but also provides guidelines for optimizing their conductive properties that can be readily extended to other solid electrolytes. © 2021 Wiley-VCH GmbH
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.
Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning
(Elsevier, 2020-05-17) Liu, Y; Wu, JM; Wang, ZC; Lu, XG; Avdeev, M; Shi, S; Wang, CY; Yu, T
Creep rupture life is a key material parameter for service life and mechanical properties of Ni-based single crystal superalloy materials. Therefore, it is of much practical significance to accurately and efficiently predict creep life. Here, we develop a divide-and-conquer self-adaptive (DCSA) learning method incorporating multiple material descriptors for rational and accelerated prediction of the creep rupture life. We characterize a high-quality creep dataset of 266 alloy samples with such features as alloy composition, test temperature, test stress, and heat treatment process. In addition, five microstructural parameters related to creep process, including stacking fault energy, lattice parameter, mole fraction of the γ' phase, diffusion coefficient and shear modulus, are calculated and introduced by the CALPHAD (CALculation of PHAse Diagrams) method and basic materials structure-property relationships, that enables us to reveal the effect of microstructure on creep properties. The machine learning explorations conducted on the creep dataset demonstrate the potential of the approach to achieve higher prediction accuracy with RMSE, MAPE and R2 of 0.3839, 0.0003 and 0.9176 than five alternative state-of-the-art machine learning models. On the newly collected 8 alloy samples, the error between the predicted creep life value and the experimental measured value is within the acceptable range (6.4486 h–40.7159 h), further confirming the validity of our DCSA model. Essentially, our method can establish accurate structure-property relationship mapping for the creep rupture life in a faster and cheaper manner than experiments and is expected to serve for inverse design of alloys. © 2020 Acta Materialia Inc. Published by Elsevier Ltd.
Relationships between Na+ distribution, concerted migration, and diffusion properties in rhombohedral NASICON
(Wiley, 2020-06-24) Zou, ZY; Ma, N; Wang, AP; Ran, YB; Song, T; Jiao, Y; Zhou, H; Shi, W; He, B; Wang, D; Li, YJ; Avdeev, M; Shi, S
Rhombohedral NaZr2(PO4)3 is the prototype of all the NASICON-type materials. The ionic diffusion in these rhombohedral NASICON materials is highly influenced by the ionic migration channels and the bottlenecks in the channels which have been extensively studied. However, no consensus is reached as to which one is the preferential ionic migration channel. Moreover, the relationships between the Na+ distribution over the multiple available sites, concerted migration, and diffusion properties remain elusive. Using ab initio molecular dynamics simulations, here it is shown that the Na+ ions tend to migrate through the Na1–Na3–Na2–Na3–Na1 channels rather than through the Na2–Na3–Na3–Na2 channels. There are two types of concerted migration mechanisms: two Na+ ions located at the adjacent Na1 and Na2 sites can migrate either in the same direction or at an angle. Both mechanisms exhibit relatively low migration barriers owing to the potential energy conversion during the Na+ ions migration process. Redistribution of Na+ ions from the most stable Na1 sites to Na2 on increasing Na+ total content further facilitates the concerted migration and promotes the Na+ ion mobility. The work establishes a connection between the Na+ concentration in rhombohedral NASICON materials and their diffusion properties. © 1999-2021 John Wiley & Sons, Inc.
Software for evaluating ionic conductivity of inorganic–polymer composite solid electrolytes
(American Association for the Advancement of Science, 2023-01) Ding, Y; He, B; Wang, D; Avdeev, M; Li, YJ; Shi, S
Inorganic–polymer composite solid electrolytes (IPCSEs) obtained by filling the polymer matrix with inorganic materials usually have higher ionic conductivity compared with individual phases. This important increase in ionic conductivity is explained in terms of the new percolation paths formed by the highly conductive interface between inorganic filler and polymer. The conduction in such systems can be investigated using the effective medium theory (EMT) and random resistance model (RRM). EMT can be used to analyze the effect of filler size on the ionic conductivity of disordered IPCSEs, while RRM can describe the composites with inorganic fillers of various shapes (nano-particles, nano-wires, nano-sheets, and nano-networks) in ordered or disordered arrangement. Herein, we present software evaluating the ionic conductivity in IPCSEs by combining EMT and RRM. The approach is illustrated by considering the size, shapes, and arrangements of inorganic fillers. The ionic conductivities of different types of IPCSEs are predicted theoretically and found in good agreement with the experimental values. The software can be used as an auxiliary tool to design composite electrolytes. © 2023 Yuqing Ding et al. Exclusive licensee Beijing Institute of Technology Press. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0)
Ultrastable all-solid-state sodium rechargeable batteries
(American Chemical Society, 2020-08-11) Yang, J; Liu, G; Avdeev, M; Wan, H; Han, F; Shen, L; Zou, Z; Shi, S; Hu, YS; Wang, CS; Yao, X
The insufficient ionic conductivity of oxide-based solid electrolytes and the large interfacial resistance between the cathode material and the solid electrolyte severely limit the performance of room-temperature all-solid-state sodium rechargeable batteries. A NASICON solid electrolyte Na3.4Zr1.9Zn0.1Si2.2P0.8O12, with superior room-temperature conductivity of 5.27 × 10–3 S cm–1, is achieved by simultaneous substitution of Zr4+ by aliovalent Zn2+ and P5+ by Si4+ in Na3Zr2Si2PO12. The bulk conductivity and grain boundary conductivity of Na3.4Zr1.9Zn0.1Si2.2P0.8O12 are nearly 20 times and almost 50 times greater than those of pristine Na3Zr2Si2PO12, respectively. The FeS2||polydopamine-Na3.4Zr1.9Zn0.1Si2.2P0.8O12||Na all-solid-state sodium batteries, with a polydopamine modification thin layer between the solid electrolyte and the cathode, maintain a high reversible capacity of 236.5 mAh g–1 at a 0.1 C rate for 100 cycles and a capacity of 133.1 mAh g–1 at 0.5 C for 300 cycles, demonstrating high performance for all-solid-state sodium batteries. © 2020 American Chemical Society
Uncovering the potential of M1‐site‐activated NASICON cathodes for Zn‐Ion batteries
(Wiley, 2020-02-20) Hu, P; Zou, Z; Sun, XW; Wang, D; Ma, J; Kong, QY; Xiao, DD; Gu, L; Zhou, XH; Zhao, JW; Dong, SM; He, B; Avdeev, M; Shi, S; Cui, GL; Chen, LQ
There is a long‐standing consciousness that the rhombohedral NASICON‐type compounds as promising cathodes for Li+/Na+ batteries should have inactive M1(6b) sites with ion (de)intercalation occurring only in the M2 (18e) sites. Of particular significance is that M1 sites active for charge/discharge are commonly considered undesirable because the ion diffusion tends to be disrupted by the irregular occupation of channels, which accelerates the deterioration of battery. However, it is found that the structural stability can be substantially improved by the mixed occupation of Na+/Zn2+ at both M1 and M2 when using NaV2(PO4)3 (NVP) as a cathode for Zn‐ion batteries. The results of atomic‐scale scanning transmission electron microscopy, analysis of ab initio molecular dynamics simulations, and an accurate bond‐valence‐based structural model reveal that the improvement is due to the facile migration of Zn2+ in NVP, which is enabled by a concerted Na+/Zn2+ transfer mechanism. In addition, significant improvement of the electronic conductivity and mechanical properties is achieved in Zn2+‐intercalated ZnNaV2(PO4)3 in comparison with those of Na3V2(PO4)3. This work not only provides in‐depth insight into Zn2+ intercalation and dynamics in NVP unlocked by activating the M1 sites, but also opens a new route toward design of improved NASICON cathodes. © 1999-2021 John Wiley & Sons, Inc.

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