<b>Fluoride-ion batteries are a promising alternative to lithium-ion batteries with higher theoretical capacities and greater global abundance, but they have experienced limited success due to poor ionic conductivities and stability of known electrolytes and electrodes. Here, we report on the high-throughput computational screening of 9,747 fluoride-containing compounds in search of fluoride-ion conductors. Via a combination of empirical, light-weight DFT, and NEB calculations, we were able to identify several new crystal systems and compounds as promising candidates for fluoride-ion conductivity. We applied a search strategy where calculations are performed in any order (“decoupled”), predictive models are repeatedly updated during the search (“iterative”), and computational resources are reassigned as models change and materials are re-ranked (“dynamic”). This strategy allowed us to build a diverse set of 287 mid-point-only NEB barriers from which heuristics for fluoride-ion diffusion could be developed and evaluated. For example, these data are used to empirically correct 6,797 pathway barriers calculated via DFT under static conditions and smaller supercells. Further, this larger pool of approximated barriers were used to assess fundamental models based on pathway length and change in coulombic environment, which allowed rapid characterization of &gt;40,000 symmetrically unique pathways. Our final dataset can be queried using diverse criteria and therefore aids in the discovery of novel materials in various applications such as batteries or even electrides. This led us to identify promising materials with low activation energies for transport, including several new structure-types for fluoride conductors. Together, these results will guide future investigations on fluoride-ion conductors, while the new heuristics will facilitate future high-throughput studies for ion conductors.</b>

High-throughput screening for fluoride-ion conductors via a decoupled, dynamic, and iterative framework

ACS Spring 2022

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