Various techniques exist to reduce high-dimensional data to make visualisation easier for us to understand. In Figure , we have chosen the uniform manifold approximation and projection (UMAP) and the t-stochastic neighbours embedding (t-SNE) . Visualising the embeddings can provide a qualitative understanding of the quality of the learned representations. It is apparent that we can recover expected chemical trends but also find patterns that we would not intuitively ex- pect. For example, in Figure , we find that the halides with the exception of the chloride ion appear close to each other within this space with Br -and I -close to each other than with F -. All the halides cluster together when t-SNE is used for the dimension reduction as shown in Figure (the iodide and bromide anions overlap each other in the reduced space).