Practically, to represent equation 2.4, we must avoid problems of having a variant number of neighbors as we cannot handle data of various sizes without reverting back to graph neural networks. Additionally, we must ensure our representation for the neighborhood is unique to each chemical neighborhood. To avoid these problems we use a data structure whereby the chemical neighborhood feature space is described by embedding-sized placeholders for the existence of H, C, N, O, and/or F neighbor, in that order. For instance, being an oxygen atom in an alcohol, will fill the carbon and the hydrogen embedding placeholders for each update (while leaving the rest as zero), with either some previous update or with the initial average for that neighboring element. If, for instance, two carbons are found as neighbors, then their embeddings are added onto the same placeholder for the carbon neighbor. This practical approach avoids issues of variant neighborhood sizes, as now at each update the number of features is the same, D embedding × D elements . If there are multiple neighborhood depths included in the representations, then the order of the neighbor elements is repeated for each depth such that we have D embedding × D elements × D depth . This ensures uniqueness in the representation even across multiple layers of neighborhood depth.