Crystallographic prediction from diffraction and chemistry data for higher throughput classification using machine learning

Simultaneously capturing material structure and chemistry in the form of accessible data is often advantageous for drawing correlations and enhancing our understanding of measurable materials behavior and properties. Unfortunately, in many cases, accessing data at the scale required, is highly multidimensional and sparse by the historical and evolving nature of materials science. To mitigate difficulties, we develop and employ methods of data analytics in conjunction with open accessible chemistry and structure datasets, to classify and reduce the amount of data needed for extracting useful descriptors from multidimensional techniques. The construction and systematic ablation of our model highlights the potential for dimensional reduction in data sampling, improved classification, and identification of correlations among material crystallography and chemistry.