Finally, we adopt a classification task, inspired by recent work investigating machine learning extrapolation capabilities in materials informatics . We first label material instances with the corresponding property in the top 20% of the distribution as extraordinary. Here, the term 'top' is defined based on the specific property under consideration. In some cases, 'top' refers to the highest values, while in other cases, 'top' denotes the lowest values, depending on the tail of the distribution. We finally consider logistic regression as a simple binary classifier to differentiate ordinary from extraordinary materials.