Hauptseite > Publikationsdatenbank > One‐Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity** > print

001     1019515
005     20240712113122.0
024 7 _ |a 10.1002/batt.202200228
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024 7 _ |a 10.34734/FZJ-2023-05462
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037 _ _ |a FZJ-2023-05462
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100 1 _ |a Rahmanian, Fuzhan
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245 _ _ |a One‐Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity**
260 _ _ |a Weinheim
|c 2022
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520 _ _ |a 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.
536 _ _ |a 1221 - Fundamentals and Materials (POF4-122)
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536 _ _ |a BIG-MAP - Battery Interface Genome - Materials Acceleration Platform (957189)
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700 1 _ |a Vogler, Monika
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700 1 _ |a Wölke, Christian
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700 1 _ |a Yan, Peng
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700 1 _ |a Winter, Martin
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700 1 _ |a Cekic-Laskovic, Isidora
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700 1 _ |a Stein, Helge S.
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773 _ _ |a 10.1002/batt.202200228
|g Vol. 5, no. 10, p. e202200228
|0 PERI:(DE-600)2897248-X
|n 10
|p e202200228
|t Batteries & supercaps
|v 5
|y 2022
|x 2566-6223
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