Electrolyte solutions play a vital role in a vast range of important materials chemistry applications. For example, they are a crucial component in batteries, fuel cells, supercapacitors, electrolysis and carbon dioxide conversion/capture. Unfortunately, the determination of even their most basic properties from first principles remains an unsolved problem. As a result, the discovery and optimisation of electrolyte solutions for these applications largely relies on chemical intuition, experimental trial and error or empirical models. The challenge is that the dynamic nature of liquid electrolyte solutions require long simulation times to generate trajectories that sufficiently sample the configuration space; the long range electrostatic interactions require large system sizes; while the short range quantum mechanical (QM) interactions require an accurate level of theory. Fortunately, recent advances in the field of deep learning, specifically neural network potentials (NNPs), can enable significant accelerations in sampling the configuration space of electrolyte solutions. Here, we outline the implications of these recent advances for the field of materials chemistry and identify outstanding challenges and potential solutions.