In response to these challenges, in this work we aim to develop a systematic workflow that combines both experimental and computational methods to perform accurate prediction and virtual screening for AZIB electrolytes. Following common practice in the development of classical FF, easily measurable experimental data such as densities and evaporation enthalpies are used to generate classical FF that are specifically tailored for aqueous electrolyte systems. However, unlike conventional hand-crafted parameter tweaking, multimodal top-down optimization is conducted using the advanced automatic differentiable (AD) MD (ADMD) technique. This new technique allows us to optimize FF automatically without any human intervention. ADMD transforms top-down FF fine-tuning from a highly specialized, tedious, and time-consuming task into a task that can be conducted automati-cally and efficiently. This enables us to use any available experimental data to update the FF on a routine basis, which is important for the systematic exploration of the electrolyte design space. The optimized FF is then used to screen the AZIB electrolyte formulae, targeting the electrical conductivity (especially at low temperatures) as the main property to optimize. In comparison with the experimental data, we further find that the ionic conductivity predicted using optimized FF is an excellent predictor for the freezing of electrolytes.