  Ionic liquids (ILs) are an exciting class of electrolytes finding
applications in many areas from energy storage to solvents, where they have
been touted as ``designer solvents'' as they can be mixed to precisely tailor
the physiochemical properties. As using machine learning interatomic potentials
(MLIPs) to simulate ILs is still relatively unexplored, several questions need
to be answered to see if MLIPs can be transformative for ILs. Since ILs are
often not pure, but are either mixed together or contain additives, we first
demonstrate that a MLIP can be trained to be compositionally transferable,
i.e., the MLIP can be applied to mixtures of ions not directly trained on,
whilst only being trained on a few mixtures of the same ions. We also
investigate the accuracy of MLIPs for a novel IL, which we experimentally
synthesize and characterize. Our MLIP trained on $\sim$200 DFT frames is in
reasonable agreement with our experiments and DFT.
