High-throughput discovery of fluoride-ion conductors via a decoupled, dynamic, and iterative (DDI) framework
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Inorganic Fluorides and Related Compounds · Machine Learning in Materials Science · Inorganic Chemistry and Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (71)
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