For chemical statistical models this comes with promising consequences. Firstly, it means that fundamental stoichiometric reactions can be modelled with vector algebra thereby opening a new way to traverse the chemical space in an algebraically structured way. Most significantly, and in contrast to NLP, the structure of the chemical embedding space is not a consequence of the social construct of language. Rather, it relates quantitatively to the underlying chemical structure. In other words, for a chemical GNN model, we can replicate the embedding space of chemistry by involving the notion of locality and chemical environments. We demonstrate how this can be achieved using perturbational updates that are based on the composition of consecutively layered neighborhood environments. Simultaneously, this leads to a natural interpretation of how atomistic and extensive properties are deduced from GNN models based on neighborhood composition.