Learning the laws of lithium-ion electrolyte transport using symbolic regression

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 surrogate 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 prove the potential of using machine learning with HTE to find accurate and physically-sound models in complex systems without established physico-chemical theories.