In this work, we made an effort to learn word embedding from the structural information described by the Robocrystallographer description for all the elements of the periodic table. We tested the resulting embeddings against the original mat2vec features and one-hot encodings. The test indicates that our word embeddings demonstrate learning of domain knowledge. As a further research direction, we will suggest representing the descriptions from Robocrystallographer in a less monotonic way and taking advantage of phrase detection. Also learning the numerical properties of numbers along with the semantic relationships between words will help encode the structural knowledge to a great deal.