Electrolytes typically consist of salts, solvents, and additives. The salts provide ions for charge transport, and solvents provide the basic solvation environment, while additives are used to further fine-tune the electrolyte properties. To obtain better electrolytes, one can either change the molecular structures or fine-tune the composition ratio of all components, leading to a gigantic design space. Formula optimization based on trial-and-error and human intuition can be very inefficient, so systematic screening is often necessary, which is, however, still a quite challenging task. Currently, both experimental and simulation approaches can be used for high-throughput screening. Experimental approaches (especially in vivo tests) are reliable but can be economically expensive and time consuming on a large scale. More importantly, macroscopic experimental tests do not provide detailed microscopic structural and dynamic information. This information is needed to understand the structure-property relationship, which is necessary for the rational design of electrolytes. In contrast, computational tools such as molecular dynamics (MD) are more cost-effective and informative on the atomic scale. However, MD simulation is haunted by inaccurate force fields (FF) and sampling problems, rendering its prediction untrustworthy. Moreover, MD simulation is largely limited to predicting basic homogeneous bulk physical properties. However, complex phenomena such as interfacial chemistry and phase transitions involving large time and spatial scales have profound impacts on battery performance. Simulating these phenomena directly using MD is extremely non-trivial and is still nearly impossible to do in high-throughput. To solve these problems and gain a more comprehensive understanding, both experimental and computational tools need to be combined.