Searching for ductile superconducting Heusler X2YZ compounds
Noah Hoffmann, Tiago F. T. Cerqueira, Pedro Borlido, Antonio Sanna,, Jonathan Schmidt, Miguel A. L. Marques

TL;DR
This study combines ab initio calculations and machine learning to identify ductile Heusler compounds with potential superconductivity above 10 K, expanding the known family of superconductors.
Contribution
It introduces a comprehensive computational and machine learning approach to discover new superconducting Heusler compounds with high critical temperatures and ductility.
Findings
Identified 8 hypothetical Heusler materials with Tc > 10 K.
Most candidate materials are predicted to be highly ductile.
Universal behaviors in superconducting and elastic properties were observed.
Abstract
Heusler compounds have always attracted a great deal of attention from researchers thanks to a wealth of interesting properties for technological applications. They are intermetallic ductile compounds, and some of them have been found to be superconducting. With this in mind, we perform an extensive study of the superconducting and elastic properties of the cubic (full-)Heusler family. Starting from thermodynamically stable compounds, we use ab initio methods for the calculation of the phonon spectra, electron-phonon couplings, superconducting critical temperatures and elastic tensors. By analyzing the statistical distributions of these properties and comparing them to anti-perovskites we recognize universal behaviors that should be common to all conventional superconductors while others turn out to be specific to the material family. The resulting data is used to train interpretable…
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Taxonomy
TopicsHeusler alloys: electronic and magnetic properties · Machine Learning in Materials Science · Advanced Thermoelectric Materials and Devices
