Superconductivity in antiperovskites

We present a comprehensive theoretical study of conventional superconductivity in cubic antiperovskites materials with composition XYZ(3) where X and Z are metals, and Y is H, B, C, N, O, and P. Our starting point are electron-phonon calculations for 397 materials performed with density-functional perturbation theory. While 43% of the materials are dynamically unstable, we discovered 16 compounds close to thermodynamic stability and with T-c higher than 5 K. Using these results to train interpretable machine-learning models, leads us to predict a further 57 (thermodynamically unstable) materials with superconducting transition temperatures above 5 K, reaching a maximum of 17.8 K for PtHBe3. Furthermore, the models give us an understanding of the mechanism of superconductivity in antiperovskites. The combination of traditional approaches with interpretable machine learning turns out to be a very efficient methodology to study and systematize whole classes of materials and is easily extendable to other families of compounds or physical properties.

Automated summary

This comprehensive study investigates the conventional superconductivity in cubic antiperovskites materials and uses interpretable machine-learning models to predict materials with high superconducting transition temperatures. The combination of traditional approaches and machine learning proves to be an efficient methodology for studying and systematizing materials.