One-shot active learning for globally optimal battery electrolyte conductivity

31 May 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Non-aqueous aprotic battery electrolytes, among other requirements, need to perform well over a wide range of temperatures in practical applications. Herein we present a one-shot active learning study to find all conductivity optima, confidence bounds, and relating trends in the temperature range from 30 °C to 60 °C. This optimization is enabled by a high-throughput formulation and characterization setup guided by one-shot active learning utilizing robust and heavily regularized polynomial regression. Whilst there is an initially good agreement for intermediate and low temperatures, there is a need for the active learning step to globally improve the model. Optimized electrolyte formulations likely correspond to the highest physically possible conductivities within this system when compared to literature data. A thorough error propagation analysis yields a fidelity assessment of conductivity measurements and electrolyte formulation.


machine learning
active learning

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