We think this descriptor could be improved further by taking two steps. First, the descriptions we get from Robocrystallographer are very monotonic. So we could not consider frequently occurring groups of words as phrases. In the original paper we saw that including phrases as individual tokens had a significant impact on the quality of embedding. We could harness that advantage by making the Robocrystallographer description less monotonic like literature abstracts in some way. Secondly, Robocrystallographer description contains crystal structure descriptions that are mostly numerical in nature. Mat2vec and other word embedding models such as GloVe , word2vec are not as effective in capturing numeric properties associated with numbers as capturing semantic relationships between words that can lead to misinterpretation. For instance, they do not contain any prior information about the magnitude of the number they are encoding and always demonstrate similarities with words while they are not supposed to be related. To deal with the inefficiency of handling numeration and magnitude of numbers many models mask the numbers and overlook them, mat2vec is not any different. In our case, the ability to capture this numerical information along with the semantic relations would drastically improve the quality of encoded structural information in our descriptor.