To tackle this challenge, DL can be useful for accelerating the electrolyte design, both for simulation and experiment. Here we first show some cases for DL-accelerated numerical simulations. Among all simulations methods, molecular dynamic (MD) is the most popular tool to simulate liquid electrolytes. In the electrolyte system, ions lead to a strong local electric field and cause the polarization of the solvent molecules, especially for highly concentrated electrolytes. These polarization terms influence the electrolyte transport properties a lot and therefore need to be predicted by MD. (101) This prediction can be effectively accelerated by implementing NN to learn the atomic polarizabilities and charges (102). Such an algorithm is only dependent on connectivity of the atoms within a molecule, so the dependencies on the 3D conformation can be avoided. Apart from this, researchers also informed the neural networks with the surrounding environment of the atoms or molecules to improve the accuracy and speed of the prediction. (Figure ) (103, 104) These methods offer a widely applicable and automated tool to comprehend atomic-level dynamics in material systems, considering the vast quantities of molecular dynamics data produced daily in almost all areas of materials design. This provides more insights of the electrolyte properties without completely screening the whole parameter space or synthesizing all possible electrolyte candidates, even in shortage of some physical interactions.