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The Heusler compounds have provided a playground of material candidates forvarious technological applications based on their highly diverse and tunableproperties, controlled by chemical composition and crystal structure. However,physical exploration of the Heusler chemical space en masse is impossible inpractice, hindering the exploration of the chemical composition vs. proprietiesrelationship. Many of these applications are related to the Heuslers electrontransport characteristics, which are embedded in their electronic bandstructure (EBS). Here we we created a Heuslers dataset using the MaterialsProject (MP) database -- retrieving both chemical composition and their EBSs.We then used machine learning to develop a model correlating the compositionvs. number of Dirac points in the EBS for Heuslers and also other Cubiccompounds by identifying said Dirac points using an automated algorithm as wellas generating chemical composition and global crystal structure features. OurML model captures the overall trend, as well as identifies significantelectronic and global crystal structure features, however, the ML modelsuffered from significant variance due to the lack of site specific features.Future work on a methodology for handling atomic site specific features willallow ML models to better match the underlying quantum mechanics governing theproperties (also based on site specific properties) and capture the electronicproperties in a more generalized approach.

Electronic band structure screening for Dirac points in Heuslers