TY  - JOUR
AU  - Flores, Eibar
AU  - Wölke, Christian
AU  - Yan, Peng
AU  - Winter, Martin
AU  - Vegge, Tejs
AU  - Cekic-Laskovic, Isidora
AU  - Bhowmik, Arghya
TI  - Learning the laws of lithium-ion transport inelectrolytes using symbolic regression†
JO  - Digital discovery
VL  - 1
IS  - 4
SN  - 2635-098X
CY  - Washington DC
PB  - Royal Society of Chemistry
M1  - FZJ-2023-05466
SP  - 440-447
PY  - 2022
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001101457500001
DO  - DOI:10.1039/D2DD00027J
UR  - https://juser.fz-juelich.de/record/1019519
ER  -