%0 Journal Article
%A Rahmanian, Fuzhan
%A Vogler, Monika
%A Wölke, Christian
%A Yan, Peng
%A Winter, Martin
%A Cekic-Laskovic, Isidora
%A Stein, Helge S.
%T One‐Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity**
%J Batteries & supercaps
%V 5
%N 10
%@ 2566-6223
%C Weinheim
%I Wiley-VCH
%M FZJ-2023-05462
%P e202200228
%D 2022
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000844221600001
%R 10.1002/batt.202200228
%U https://juser.fz-juelich.de/record/1019515