In the large data regime, neural network-based models have been able to learn representations of the elements. In addition to these learnt descriptors of the elements, hand-crafted representations of elements and compositions continue to play a role in materials informatics for property prediction . An often overlooked aspect when dealing with traditional element representations is the role of ions. Ions, and the knowledge of oxidation states, play a significant role in both the structural and electronic properties of materials such as electrical conductivity , chemical bonding, and magnetism as a result of their electronic configuration differing from the parent atom . For example, Fe 0 is the building block of ferromagnetic iron metal, Fe 3+ is found in the antiferromagnetic insulator Fe 2 O 3 (hematite), while a mixture of Fe 2+ and Fe 3+ is found in the ferrimagnetic crystals of Fe 3 O 4 (magnetite).