Next generation batteries will require new electrolyte concepts. Two interesting alternatives are ionic liquids (ILs) and high entropy electrolytes, molten salts (MSE). However, the chemical space of these materials are vast and it is impossible to fully explore them using classical methods. In this project, we will model ILs and MSE on a molecular level using machine learning and symbolic regression. A particular aim is to relate the molecular structures to macroscopic properties that are relevant for batteries, such as ionic conductivity. Previous works and initial experiments have shown that graph neural networks can be successful in these tasks - but to take the work further more computational resources will be needed.