Another alternative option is machine learning MD (MLMD), which learns a ML PES from a large number of ab initio data. MLMD is relatively faster compared to AIMD while retaining its accuracy, but the current ML PES does not possess the transferability needed for formula screening. There are large models such as BAMBOO and MACE-OFF23, aimed at training a general electrolyte ML PES. But such efforts still only exist for commercial organic electrolytes, with compromised accuracy and limited chemical space coverage. Other hybrid approaches such as PhyNEO 24 need ab initio many-body data for training, which is also too expensive for the screening of aqueous electrolytes. Due to these reasons, the main workhorse in this area is still the classical fixed point charge FF, the main advantage of which is the exceptional simulation speed. With carefully fine-tuned parameters, the classical FF can be accurate enough for the purpose of large-scale screening, but the parameter optimization process is highly non-trivial. As we will show in this work, the off-the-shelf FF can be qualitatively insufficient for any screening tasks.