  The expansiveness of compositional phase space is too vast to fully search
using current theoretical tools for many emergent problems in condensed matter
physics. The reliance on a deep chemical understanding is one method to
identify local minima of relevance to investigate further, minimizing sample
space. Utilizing machine learning methods can permit a deeper appreciation of
correlations in higher order parameter space and be trained to behave as a
predictive tool in the exploration of new materials. We have applied this
approach in our search for new high temperature superconductors by
incorporating models which can differentiate structural polymorphisms, in a
pressure landscape, a critical component for understanding high temperature
superconductivity. Our development of a representation for machine learning
superconductivity with structural properties allows fast predictions of
superconducting transition temperatures ($T_c$) providing a $r^2$ above 0.94.
