We trained our models on different sample sizes: values ranging between 10-90% of the total dataset was set aside as the training set for all the chosen tasks. From figure we can see that our model is always giving better error values than the model using one-hot encodings. Furthermore, when the training set size is smaller, our model gives a very similar error as the model constructed with the original mat2vec features. Both of these models show better performance with respect to the model constructed with one-hot encoding in the realm of small data as expected. In addition, the error value of our model stays closer to the model with original mat2vec features across the plot for all these regression tasks. This suggests that our features were encoded with underlying structural domain knowledge and similar to the original mat2vec model, our model outperformed the one-hot model while learning from both a smaller sample size and the entire dataset.