Machine Learning-Driven Mass Discovery and High-Throughput Screening of Fluoroether-Based Electrolytes for High-Stability Lithium Metal Batteries.

Developing novel fluoroether electrolytes with high-voltage stability is an effective strategy to improve the performance of lithium metal batteries (LMB). However, the vast chemical space of fluoroether is underexplored due to the absence of effective tools to evaluate the potential used in high-voltage LMB. Herein, a framework was developed in combination of Voting ensemble algorithms and graph convolution neural network (GCNN), allowing the fast assessment of oxidative stability of non-aqueous liquid electrolytes, synthesizability of solvents as well as the solvation ability of them to dissolve lithium salts. Potential fluoroether solvent candidates for high-voltage LMB were screened out from a virtual library comprising 5576 electrolytes constructed by a combination of 1510 solvents and 4 salts. Among them, two fluorinated ethers, 1,1,1,3,3,3-hexafluoro-2-(2-methoxyethoxy) propane and 7,7,8,8-tetrafluoro-3,12-dimethoxy-2,5,10,13-tetraoxatetradecane, were successfully synthesized and showed satisfactory high-voltage stability, sufficient solvation ability and satisfactory cycling with almost 99.5 % coulombic efficiency in Li||NMC811 full cell. This work provided an efficient framework for the discovery of solvents with high-voltage tolerance in a vast structural space prior to experimental synthesis, accelerating the development of advanced electrolyte for high-energy-density rechargeable batteries.