| Summary: | Abstract We present a comprehensive theoretical study of conventional superconductivity in cubic antiperovskites materials with composition XYZ3 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.
|
|---|