(362f) Design of Electrolyte Molecules for Lithium Batteries

Lithium batteries (LBs) have closely integrated into sustainable energy systems by powering electronic devices and facilitating grid-scale energy storage. Meanwhile, the expanding requirements and the diversification of application scenarios appeal to next-generation LBs with superior performance, including fast-charging capability, all-climate adaptability, high energy density, and long lifespan. This puts forward increasingly high demands on battery materials to shape future advanced LBs.

A typical LB is composed of two electrodes and a liquid electrolyte. The liquid electrolyte functions as the “blood” of a battery, connecting the two electrodes and serving as the medium for ion transport. Its properties play a direct role in determining the practical performance of the battery as opposed to the theoretical upper limit set by the electrodes. Therefore, electrolyte design is perceived as the primary solution to satisfying the requirements of multifarious applications and constructing high-performance LBs.

However, electrolytes are complex systems which consist of various components. Researchers have adopted conventional trial-and-error methods to study thousands of molecules over the past years, but only dozens of molecules haven been found to be working in LB electrolytes. Considering the vast molecular space, this time-expense and low-efficiency approach renders it impractical to meet the urgent and diverse demands for electrolytes in the future, which presents both a significant opportunity and a formidable challenge for the electrolyte design.

Here, we integrate multiscale simulations and experimental methods to design electrolytes, including elucidating the structure–property relationships of electrolytes, evaluating electrolyte properties by computational methods, and designing electrolytes molecules according to target needs. First, the stable cycling of a battery is the prerequisite for satisfying all other requirements, so the electrolyte stability is focused on primarily. Models of the electrolyte solvation structures were constructed to investigate the dependence of the electrolyte stability on electrolyte structures. By investigating on around 1400 systems, it is revealed that the electrolyte oxidative and reductive stability increases and decreases, respectively, as long as the Li⁺–molecule solvation structures are formed. This emphasizes the significance of considering the solvation structures instead of stand-alone molecules in evaluating the electrolyte stability. Second, a high-throughput electrolyte calculation (HTEC) software is self-developed. HTEC consists of 8-round density functional theory calculations and molecular dynamics simulations, which can achieve a convenient and efficient evaluation of over 20 kinds of electrolyte properties including stability, viscosity, conductivity, etc. An electrolyte database contains over 200,000 entries have been constructed for now, which is the biggest one around the world to the best our knowledge. Third, electrolyte design based on different application requirements is carried out by target-orientated screening of molecules. Taking the design of fast-charging LB electrolytes at high temperatures as an example, the thermal stability, chemical stability, binding strength to Li⁺, viscosity, boiling point, etc., which play significant role in determining the fast-charging performance of electrolytes at high temperatures, are considered. Two promising molecules in carboxylic ester structures are discovered by the stepwise screening according to property criteria. These two novel molecules render superior battery performance compared with the commercialized LB electrolytes (ethylene carbonate/dimethyl carbonate/lithium hexafluorophosphate-based).

To conclude, our work establishes quantitative structure–property relationships and leveraging the high-throughput advantages of computational methods to expand the molecular space under investigation, thereby expediting the design of promising molecules through a highly efficient data-driven approach. It not only has demonstrated success in the electrolyte design but also holds great potential in the design of other functional materials.