Graph neural networks (GNNs) have recently emerged as a popular representation method for molecular and crystal structures due to their inherent topological representation and flexibility of node and edge featurisation. Nevertheless, due to the local nature of GNNs, chemical systems can only be incompletely represented depending on the number of stacked layers in the architecture, with longer-range interactions often being overlooked within the molecular graph. We exemplify the improtance of this issue by considering two similar molecular structures in Figure . In a standard message-passing scheme such as an MPNN the carbon-carbon bonding of the two structures would be represented identically for both graphs (both during the forward and backward pass), missing the global context during the edge update. Evidently, capturing the global environment and context of molecular structures is of utmost importance when modeling materials science prediction tasks.