Data-driven automated robotic experiments accelerate discovery of multi-component electrolyte for rechargeable Li–O2 batteries

Abstract

Rechargeable aprotic lithium–oxygen (Li–O 2 ) 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 LiNO 3 , 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–O 2 batteries, realizing stability over 100 cycles with capacity of 0.5 mAh/cm 2 . 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. • Automated electrochemical experiments for performance evaluation of electrolytes • Efficient searching by machine-learning methodologies using Bayesian optimization • Multi-component electrolyte additives for lithium–oxygen batteries are discovered • Stable solid electrolyte interface formed via synergy among the various components Multi-component electrolyte additives for lithium–oxygen batteries for improving the cycle performance are discovered by automated high-throughput robotic experiments with machine-learning methodologies using Bayesian optimization. This work by Matsuda et al., empowered by data-driven high-throughput-screening methods, may offer new opportunities for efficient identification of electrolyte compositions and acceleration of the development of next-generation rechargeable batteries.