  The Heusler compounds have provided a playground of material candidates for
various technological applications based on their highly diverse and tunable
properties, controlled by chemical composition and crystal structure. However,
physical exploration of the Heusler chemical space en masse is impossible in
practice, hindering the exploration of the chemical composition vs. proprieties
relationship. Many of these applications are related to the Heuslers electron
transport characteristics, which are embedded in their electronic band
structure (EBS). Here we we created a Heuslers dataset using the Materials
Project (MP) database -- retrieving both chemical composition and their EBSs.
We then used machine learning to develop a model correlating the composition
vs. number of Dirac points in the EBS for Heuslers and also other Cubic
compounds by identifying said Dirac points using an automated algorithm as well
as generating chemical composition and global crystal structure features. Our
ML model captures the overall trend, as well as identifies significant
electronic and global crystal structure features, however, the ML model
suffered from significant variance due to the lack of site specific features.
Future work on a methodology for handling atomic site specific features will
allow ML models to better match the underlying quantum mechanics governing the
properties (also based on site specific properties) and capture the electronic
properties in a more generalized approach.
