Accelerating the Search for Stable Full Heusler Compounds through Machine Learning

Abstract

Applications for Heusler compounds are expanding in topological insulators, magnetocaloric, spintronics, and superconductivity areas. These substances are expanding the boundaries of science and offering answers to engineering problems. Our work demonstrates a discovery engine that can predict the crystal structures and chemical characteristics of 1107 Full Heusler compounds by implementing a Machine Learning approach trained with elemental descriptor data. Our approach is 50 times faster than rule-based and diffraction techniques, with a true positive rate of 0.99 for every random combination of elements on more than 1,000,000 candidates. We also compute the formation energies of these novel compounds to filter out 144 highly stable Heuslers that coincide with existing research and density functional theory trends to validate and support our findings.