Dendrite and interfacial reactions have affected zinc (Zn) metal anodes for rechargeable batteries many years. Here, these obstacles are bypassed via adopting an intrinsically safe trimethyl phosphate (TMP)‐based electrolyte to build a stable Zn anode. Along with cycling, pristine Zn foil is gradually converted to a graphene‐analogous deposit via TMP surfactant and a Zn phosphate molecular template. This novel Zn anode morphology ensures long‐term reversible plating/stripping performance over 5000 h, a rate capability of 5 mA cm−2, and a remarkably high Coulombic efficiency (CE) of ≈99.57% without dendrite formation. As a proof‐of‐concept, a Zn–VS2 full cell demonstrates an ultralong lifespan, which provides an alternative for electrochemical energy storage devices.
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.
Monoclinic natrium superionic conductors (NASICON; Na 3 Zr 2 Si 2 PO 12 ) 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.
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.
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.
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