In this work, we developed a comprehensive optimization framework to refine classical FF based on simple experimental measurements, yielding a robust theoretical model for AZIB electrolytes. By recalibrating classical MD using experimental data, we can promote the prediction accuracy to densities and electrical conductivities by orders of magnitude, enabling a reliable screening on electrolyte formulation at low temperature. More importantly,the model can be used to identify potential freezers with high efficiency, the capability of which emerged while learning to simple experimental data. Essentially, this work shows the power of combining experimental measurements with simulation tools, creating a self-reinforcing cycle that is much more capable than conventional one-shot MD simulations. Employing this method, we are able to conduct screening tasks for complex properties (such as phasetransition properties) while retaining the efficiency of classical MD. Such capability is wellevidencing for the AZIB electrolyte system in this work and is readily applicable to largerscale screening problems for other molecular materials. Therefore, We believe these efforts address critical challenges in the design of AZIB electrolytes and pave the way for their broader application.