New representations that capture chemistry (composition) and structure (periodicity) are important because they then allow us to utilize machine learning algorithms to identify and exploit patterns in data. Even the most experienced materials scientist domain expert would be unable to fully comprehend and leverage all the patterns in highdimensional materials data for hundreds of thousands of crystal structures, and then use the discovered patterns to generate novel materials. Instead, scientists have relied on very low-order approximations and simplifications for generating new materials. Or, alternatively, they have relied on screening down lists of already discovered materials to identify those candidates that most closely match the desired properties using empirical relationships or computational materials science techniques such as molecular dynamics, Density Functional Theory (DFT), etc. However, given the immense size of the chemical space that is believed to exist (10 60 materials) and our current microscopic subset of known materials (10 5 -10 6 materials) , it is unlikely that screening efforts alone are sufficient to find the new materials necessary to answer society's most pressing technological needs. Therefore it is abundantly clear that an intuitive and efficient method is needed to scan over the vast regions of untapped chemical space and select the groupings of materials it deems as stable and synthesizable.