The SkipSpecies approach for learning representations of ionic species is that a "fake" learning task is used to predict the species which co-occur with a target species in a given structure. This task is referred to as "fake" since the aim is not to build a classifier for predicting what species will co-occur with a target species but rather to use the learned matrix of parameters as an embedding table for the species within the dataset of materials.