Journal Article

FZJ-2023-05466

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Learning the laws of lithium-ion transport inelectrolytes using symbolic regression†

Flores, E. (Corresponding author) ; Wölke, C.FZJ* ; Yan, P.FZJ* ; Winter, M.FZJ* ; Vegge, T. ; Cekic-Laskovic, I.FZJ* ; Bhowmik, A. (Corresponding author)

2022
Royal Society of Chemistry Washington DC

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Please use a persistent id in citations: doi:10.1039/D2DD00027J  doi:10.34734/FZJ-2023-05466

Abstract: High-throughput experiments (HTE) enable fast exploration of advanced battery electrolytes over vast compositional spaces. Among the multiple properties considered for optimal electrolyte performance, the conductivity is critical. An analytical expression for ionic transport in electrolytes, accurate for practical compositions and operating conditions, would accelerate the process of (i) co-optimizing conductivity alongside other desirable electrolyte properties, and (ii) learning fundamental physical laws from data, which is one of the paramount goals of scientific big-data analytics. Here, we used symbolic regression with an HTE-acquired dataset of electrolyte conductivity and discovered a simple, accurate, consistent and generalizable expression. Notably, despite emerging from a purely statistical approach, the expression reflects functional aspects from established thermodynamic limiting laws, indicating our model is grounded on the fundamental physical mechanisms underpinning ionic transport. We demonstrate the potential of using machine learning with HTE to find accurate and physically-sound models in complex systems without established physico-chemical theories.

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Contributing Institute(s):

1. Helmholtz-Institut Münster Ionenleiter für Energiespeicher (IEK-12)

Research Program(s):

1. 1222 - Components and Cells (POF4-122) (POF4-122)
2. BIG-MAP - Battery Interface Genome - Materials Acceleration Platform (957189) (957189)

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