ACS Fall 2023

AI-directed discovery of high entropy electrolyte formulations for batteries using interhalogen cathodes and lithium metal anodes


The advent of data driven artificial intelligence and machine learning techniques has opened much larger design spaces for composite materials and mixtures with larger numbers of formulants. At the same time, performance benefits have been recently reported for so called “high entropy” electrolytes with large numbers of formulants resulting in more diverse set of solvation structures and enhanced charge transport kinetics in batteries with nonaqueous liquid electrolytes and lithium metal anodes. Halogen-based battery cathodes have also attracted recent interest due to their high-rate capability $(>1 mA/cm^2)$ and moderately high (200-400 mAh/g) specific capacity. The performance of halogen cathodes, due to the solution mediated nature of the conversion reactions involved, is closely coupled to both the electrolyte formulation and the solid-electrolyte interphase (SEI) layer formed on the lithium metal anode. Due to the high dependence on electrolyte formulation and many interrelated causal dependencies between cathode, electrolyte, and anode, halogen cathodes are an interesting application area for high entropy electrolytes and the use of AI to facilitate a more efficient survey of the relevant design space. In this work we report on the use of a novel artificial intelligence platform and corresponding web application to survey a space consisting of 4 salts and 4 solvents resulting in an optimized electrolyte formulation for a cell chemistry using an interhalogen cathode (I-Cl) and lithium metal anode which outperforms any electrolyte formulation currently reported in the literature for this system and showing the potential of both high-entropy electrolytes and the promise of AI to facilitate efficient searching of large formulation spaces.