The SkipSpecies ionic representations can be used to develop property prediction models with lower errors than comparable atomic representations for predicting properties such as the band gap or classifying compositions as metals and nonmetals, and magnetic or non-magnetic, suggesting that there may be some utility for ionic representations for predicting the properties of compositions. One caveat is that ionic representations are more restrictive compared to element representations, as the oxidation states in the composition have to be known to use them. While tools such as Pymatgen tions, this does introduce an additional step into a workflow to predict properties. These ionic representations may also find use for property prediction in approaches that create or generate compositions alongside knowledge of the oxidation states of the constituent elements, rather than having to decorate an already existing set of compositions where this information is not already known. One example where these ionic representations could be used is within SMACT-based workflows, as the chemical filters used to generate compositional spaces also return both the constituent elements and the oxidation states of the compositions. These representations can be used for both property prediction on these spaces, as well as providing an alternative means to visualise the compositional space as opposed to using elemental representations to visualise this space.