Data-driven automated robotic experiments accelerate discovery of multi-component electrolyte for rechargeable Li–O2 batteries
Shôichi Matsuda, G. Lambard, Keitaro Sodeyama
Abstract
Rechargeable aprotic lithium–oxygen (Li–O2) batteries are promising candidates for next-generation energy-storage devices. However, their practical application is limited by poor cycle performance because of difficulties in realizing high reaction efficiencies for both the oxygen (positive) and lithium (negative) electrodes. Herein, effective automated high-throughput robotic experiments with machine-learning methodologies using Bayesian optimization were performed to accelerate the discovery of an electrolyte suitable for realizing high reaction efficiencies for both electrodes. As a result, we identified the specific electrolyte composition (1.5 M LiNO3, 0.1 M lithium bis(trifluoromethanesulfonyl)imide, 0.1 M LiBr, 0.5 mM LiCl, and 10 mM lithium bis(oxalate)borate in dimethylamide, with 5 vol.% 1,3-dioxolane) that enhanced the discharge/charge performance of the Li–O2 batteries, realizing stability over 100 cycles with capacity of 0.5 mAh/cm2. Studies empowered by data-driven high-throughput-screening methods offer new opportunities for efficiently identifying electrolyte compositions and accelerating the development of next-generation rechargeable batteries.