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Abstract Fluoride–ion batteries are a promising alternative to lithium–ion batteries with higher theoretical capacities and working voltages, but they have experienced limited success due to the poor ionic conductivities of known electrolytes and electrodes. Here, we report a high-throughput computational screening of 9747 fluoride-containing materials in search of fluoride-ion conductors. Via a combination of empirical, lightweight DFT, and nudged elastic band (NEB) calculations, we identified >10 crystal systems with high fluoride mobility. We applied a search strategy where calculations are performed in any order (decoupled), computational resources are reassigned based on need (dynamic), and predictive models are repeatedly updated (iterative). Unlike hierarchical searches, our decoupled, dynamic, and iterative framework (DDI) began by calculating high-quality barrier heights for fluoride-ion mobility in a large and diverse group of materials. This high-quality dataset provided a benchmark against which a rapid calculation method could be refined. This accurate method was then used to measure the barrier heights for 6797 fluoride–ion pathways. The final dataset has allowed us to discover many fascinating, high-performance conductors and to derive the design rules that govern their performance. These materials will accelerate experimental research into fluoride–ion batteries, while the design rules will provide an improved foundation for understanding ionic conduction. 

The selection and design of charge integration methods remain an outstanding challenge in materials chemistry. In complex materials like electrides, this challenge is amplified by the small charge and complex shape of electride wave functions. For these reasons, popular integration methods, such as the Bader method, usually fail to assign any charge to the bare electrons in an electride. To address this challenge, we developed an algorithm that instead partitions the charge based on the electron localization function (ELF), a popular scheme for visualizing chemically important features in molecules and solids. The algorithm uses Bader segmentation of the ELF to find the electride electrons and Voronoi segmentation of the ELF to identify atoms. We apply this method, “BadELF”, to the quantification of atomic radii and oxidation states in both ionic compounds and electrides. For ionic compounds, we find that the BadELF method yields radii that agree closely with Shannon crystal radii, while the oxidation states agree closely with the Bader method. When they are applied to electrides, however, only the BadELF algorithm yields chemically meaningful charges. We argue that the BadELF method provides a useful strategy to identify electrides and obtain new insight into their most essential property: the quantity of electrons within them.