We randomly chose five MatBench tasks to test our structure-based feature vectors. We used the word embedding features obtained using mat2vec for chemical composition as input features for our models. As a control, we also built two base models for the above tasks where instead of the Robocrystallographer+mat2vec embedding vectors for chemical composition we featurized the compositions with the original mat2vec features and simple one-hot encoded vectors. Inspired by the original mat2vec paper, we used principal component analysis (PCA) to project word embeddings for Cr, Fe, and Na, their principal oxides, and space group onto two dimensions and depicted them as single points in space (see Figure ). From the relative positioning of the words, it seems that they are encoded with materials science relationships. For instance, consistent vector operations exist between words that depict concepts like 'oxide of' and 'space group of'. We present a comparison of the performance of our model that used robocrystellogra-pher+mat2vec embedding features against two base models, one of which was constructed using original mat2vec features and the other was constructed using simple one-hot encoded features (shown in figure ). Our goal was to track the change of error value the models give with respect to the size of the training set as was done by Murdock et al. . ML models are expected to give greater error values as the size of the training set decreases. One-hot encoded features only contain simple categorical information and do not contain any domain knowledge. That is why the performance of models that are constructed out of these features will deteriorate linearly as we decrease the number of examples the model can learn from.