In order to visualize the embeddings as they change from reactants to products, we project the high-dimensional vectors of the embedding space to a lower-dimensional space while incurring minimal data loss. This can be done with Principal Component Analysis (PCA) which finds the lower-dimensional space that packs the largest variance in the data.