TY  - JOUR
AU  - Rahmanian, Fuzhan
AU  - Vogler, Monika
AU  - Wölke, Christian
AU  - Yan, Peng
AU  - Winter, Martin
AU  - Cekic-Laskovic, Isidora
AU  - Stein, Helge S.
TI  - One‐Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity**
JO  - Batteries & supercaps
VL  - 5
IS  - 10
SN  - 2566-6223
CY  - Weinheim
PB  - Wiley-VCH
M1  - FZJ-2023-05462
SP  - e202200228
PY  - 2022
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000844221600001
DO  - DOI:10.1002/batt.202200228
UR  - https://juser.fz-juelich.de/record/1019515
ER  -