To evaluate the performance of the distributed representations of SkipAtom and SkipSpecies, the ElemNet 38 architecture (as chosen in the original SkipAtom paper ) with the CBFVs derived from the learnt representations as inputs is applied to two classification and two regression tasks: metallic and magnetic classification, and formation energy per atom and band gap, respectively. This work focuses on observing the difference in performance of distributed representations for elements versus atoms as SkipAtom has been shown to give superior performance to one-hot, random and Mat2Vec 39 element representations As this dataset contained polymorphs of the same composition, it was further filtered by only keeping the composition of a polymorph with the lowest energy above the convex hull. This reduced the dataset to 71,470 materials. The property datasets are depicted in Figure .