Traditionally, machine learning models in materials discovery have been descriptor-based, utilising fixed representations of molecular systems that must first be defined prior to fitting the predictive model. Deep learning approaches on the other hand allow for more flexibility in the choice of information as input due to their inherent ability to learn latent representations through raw data. This comes with the downside of less interpretability nevertheless, acting as black boxes, but often with much better accuracy. Various types of different representations can be used as input to deep models such as SMILES strings or even chemical formulas , in which the many structural and chemical motifs are implicitly extracted by the model. However, choosing suitable representations can prove challenging for problems with structurally diverse sets of materials . More recently, a move towards graph-based methods has been made as the de-facto way of representing materials, wherever such structural information is available . These methods have been based on simple aggregations of atomic numbers information together with the inherent topology of the structure -building edges amongst the atoms. Graph-based methods have generally found success in many other areas as well, including neuroscience , traffic and social network prediction .