We use coarse-grained molecular dynamics simulations to study the effect of salt concentration and host polymer molecular weight on ion transport in polymer electrolytes. We find that increasing salt concentration or molecular weight similarly slows polymer dynamics across a wide range of host polarities, and that the resulting relaxation times display a correlation to the product of the salt concentration and polymer molecular weight. However, we find that molar conductivity only decreases with polymer dynamics at high polarities but is uncorrelated with the latter at low polarities. We attribute such differences to the variation in ionic aggregation between high and low polarity electrolytes. At low polarity, ionic dissociation significantly increases with molecular weight and salt concentration, offsetting the slowdown in polymer dynamics and yielding the observed insensitivity of molar conductivity. However, at high polarity, ions are mostly dissociated, independent of either molecular weight or salt concentration, thereby strongly coupling molar conductivity to polymer dynamics.

We apply a machine learning (ML) technique to the multiobjective design of polymer blend electrolytes. In particular, we are interested in maximizing electrolyte performance measured by a combination of ionic transport (measured by ionic conductivity) and electrolyte mechanical properties (measured by viscosity) in a coarse-grained molecular dynamics framework. Recognizing the expense of evaluating each of these properties, we identify that the anionic mean-squared displacement and polymer relaxation time can serve as their proxies. By employing the ML approach known as Bayesian optimization, we identify a trade-off between ion transport and electrolyte mechanical properties as a function of varied design parameters, which include host molecular weight and polarity. Our results suggest that blend electrolytes whose hosts have unequal molecular weights, such as gel polymer electrolytes, rarely maximize electrolyte performance. Overall, our results suggest the potential of a framework to design highperformance electrolytes using a combination of molecular simulation and ML.