In this work, we developed an AZIB electrolyte screening workflow that integrates both experimental data and theoretical tools, utilizing fast and cost-effective classical MD simulations. As shown in Figure , our workflow starts with a small amount of experimental data and uses them to systematically refine the classical fixed point charge FF based on multimodal optimization. The optimization results in a significantly better prediction for thermodynamic and transport properties across different formulations and temperature ranges. In addition, we used refined FF to screen different formulations of aqueous electrolytes, expand-ing the electrolyte space, and then found the target formulations at low temperatures and experimentally verified their performance. In principle, multiple iterations can be conducted to achieve a robust and sustainable cycle, in which experimental data enhance theoretical simulations, and theoretical simulations guide further experiments in turn. The basic idea of our workflow is to use experimental data to realign the FF used in MD simulation. Tailoring FF parameters for specific application is an old idea and has been done in numerous previous works. However, these works are conducted primarily on simple systems with a limited number of adjustable parameters. For complex multicomponent systems with large number of parameters and constantly cumulating experimental data (e.g., electrolytes), the fine-tuning of FF should be performed routinely in a scalable, automatic, and reproducible fashion. ADMD simulation provides an efficient approach to carry out such a task. In this work, we use the reweighting MBAR approach implemented in our previous work to optimize the FF parameter. Easily accessible data including the evaporation enthalpies of organic additives and densities of the electrolytes at various compositions and temperatures are combined as our fitting target. Conductivities are used mainly for charge scaling realignment and validation purposes. This multimodal optimization transforms the training of the FF from years of expert handcrafting into a simple two-week process without human supervision, which is necessary to keep up with the update of the experimental data.