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Superconductivity in antiperovskites

Authors: N. Hoffmann, T.F.T. Cerqueira, J. Schmidt, and M.A.L. Marques

Ref.: npj Comput. Mater. 8, 150 (2022)

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 Tc 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.

Citations: 3 (Google scholar)

DOI: 10.1038/s41524-022-00817-4

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Bibtex:

@article{Hoffmann_2022,
	doi = {10.1038/s41524-022-00817-4},
	url = {https://doi.org/10.1038%2Fs41524-022-00817-4},
	year = 2022,
	month = {jul},
	publisher = {Springer Science and Business Media {LLC}},
	volume = {8},
	number = {1},
	author = {Noah Hoffmann and Tiago F. T. Cerqueira and Jonathan Schmidt and Miguel A. L. Marques},
	title = {Superconductivity in antiperovskites},
	journal = {npj Computational Materials}
}