Future work could focus on improving accuracy by generating a more class-balanced dataset and by better understanding of the XAS data itself, perhaps via an active learning loop. Improved target labels may also help; for example, with a coordination number of 4, the metal center could possess T d , D 4h , D 2d , C 3v , and other possible symmetries. Also, as mentioned earlier, continuous descriptors of coordination as a target property appear to produce more robust structure-property prediction . Most importantly, future models could leverage known correlations between multiple materials properties. For XAS, the intensity, shape and the energy of both pre-edge and rising edge are usually codeterminant with many more factors than coordination number, oxidation state and average bond distance alone.