To get closer to the cutting-edge battery technologies, highly concentrated electrolyte is more crucial for Zinc ion batteries because it can mitigate the hydrogen evolution reaction. Hence, equivalent methods have been utilized to examine aqueous Zinc electrolyte, which demonstrates that utilizing neural network to learn a functional physical potential is feasible even for extremely disordered systems. (105) They confirmed that the computational results accurately reproduce the observed radial distribution function and X-ray absorption near edge structure spectrum of zinc-water obtained experimentally. There were also other works that demonstrated the implementation of ML in the computational study of electrolytes. Nakayama et al. utilized an exhaustive search with a Gaussian process (ES-GP) to gauge the coordination energy. (106) This interaction energy will provide physical insights into the ionic transport of the electrolyte, which is directly related to the performance of fast-charging Li-ion batteries. Moreover, the exhaustive search can also be coupled with linear regression to become an ES-LiR model.