To explore the electrolyte formulation space, we fix the temperature at 253 K and vary the salt concentration from 1.5M to 2.5M, and the content of organic additives (EG and GI) from 10 wt. % to 40 wt. %. Evenly spaced grid points are used to sample the salt and additive concentrations, resulting in more than 400 formulations. We compute their conductivity using MD and select the three formulations with the highest conductivity for experimental validation. These results are then compared with the top three formulations with best conductivity in the original dataset at 253 K, as shown in Figure . As is shown, the conductivity errors of the model in the newly explored best performers (marked by the yellow dots in Figure ) are comparable or even smaller than the errors of the original More importantly, in the newly explored formulations, we can successfully separate the best and worst performers with high confidence. Specifically, when the predicted conductivity is lower than 10 mS/cm, it always shows an ultra-low experimental conductivity of 0.5 mS/cm, indicating a freezing or vitrification failure (labeled by the blue stars in Figure ). Therefore, even though simulating the freezing process directly is still a challenging task, we can easily identify and rule out the freezers using low liquid conductivity as an indicator, which can be computed conveniently using fast classical MD. This is physically reasonable because sluggish diffusion dynamics in a liquid is a strong sign of subsequent freezing on a longer time scale. Moreover, it is important to note that this predictive power comes from the carefully fine-tuned theoretical model based on experimental data. To show this, we also compute the conductivities of the three freezers using the normal charge-scaled model, which are labeled by the red stars in Figure . The computed conductivities by the charge-scaled model are reaching 18 mS/cm, which is completely inseparable from the best performers, thus being useless in formula exploration. Therefore, by combining a limited amount of easily measurable experimental data (such as densities and conductivities) and fast MD simulations, we effectively create a highly efficient tool to predict the complex phase-transition at low temperature. Furthermore, this tool is created fully automatically, and can be constantly self-improved using new experimental data and is free from human cherry-picking. In all, we show that with an extremely low cost, our hybrid experimental/computational method serves as an effective approach aiding the discovery of new electrolytes at low temperatures.