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| Hauptseite > Publikationsdatenbank > One‐Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity** |
| Hauptseite > Publikationsdatenbank > One‐Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity** |
| Journal Article | FZJ-2023-05462 |
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2022
Wiley-VCH
Weinheim
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Please use a persistent id in citations: doi:10.1002/batt.202200228 doi:10.34734/FZJ-2023-05462
Abstract: Non-aqueous aprotic battery electrolytes need to perform wellover a wide range of temperatures in practical applications.Herein we present a one-shot active learning study to find allconductivity optima, confidence bounds, and relating formulationtrends in the temperature range from 30°C to 60°C. Thisoptimization is enabled by a high-throughput formulation andcharacterization setup guided by one-shot active learningutilizing robust and heavily regularized polynomial regression.Whilst there is an initially good agreement for intermediate andlow temperatures, there is a need for the active learning step toimprove the model for high temperatures. Optimized electrolyteformulations likely correspond to the highest physicallypossible conductivities within this formulation system whencompared to literature data. A thorough error propagationanalysis yields a fidelity assessment of conductivity measurementsand electrolyte formulation.
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