Once Robocrystallographer had been used to generate descriptions, we used mat2vec to generate word embeddings. Prior to training mat2vec on this corpus, we needed to preprocess the input file. Although there is a built-in function in mat2vec for this task we needed to approach this differently. For instance, the original mat2vec was trained on a collection of literature abstracts that presumably didn't contain Hermann-Mauguin notations for space groups. However, Robocrystallographer descriptions contain Hermann-Mauguin space group notation for every single material. We modified ma2vec to handle these Hermann-Mauguin space group notations in the preprocessing step. Another shortcoming that Robocrystallographer description has is that the descriptions tend to be very monotonous, i.e. lacking variety in descriptive style, whereas literature abstracts have a greater variety of writing styles. The original mat2vec was constructed in such a way that if certain groups of words occur more than once in the same order, then mat2vec considers them as a phrase, and frequently occurring phrases are then considered as individual tokens. As our entire corpus contains a highly similar descriptive style even though the structures are quite distinct, there are often groups of words occurring together which would incorrectly be considered as a single phrase if we used an unaltered original version of the mat2vec model. Therefore, we could not use the phrase detection feature from the original model. We obtained feature vectors encoded with Robocrystallographer structural information for all the elements of the periodic table.