Noah Hoffmann¹, Tiago F. T. Cerqueira², Jonathan Schmidt¹, Miguel A. L. Marques^1*

1 Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, 06120 Halle (Saale), Germany.

2 CFisUC, Department of Physics, University of Coimbra, Rua Larga, 3004-516 Coimbra, Portugal

* Corresponding authors emails: miguel.marques@physik.uni-halle.de

DOI10.24435/materialscloud:6g-az [version v1]

Publication date: Apr 06, 2022

How to cite this record

Noah Hoffmann, Tiago F. T. Cerqueira, Jonathan Schmidt, Miguel A. L. Marques, Superconductivity in antiperovskites, Materials Cloud Archive 2022.49 (2022), https://doi.org/10.24435/materialscloud:6g-az

Description

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 384 materials performed with density-functional perturbation theory. While 40% of the materials were dynamically unstable as they exhibited imaginary frequencies, we discovered 16 compounds with Tc higher than 5 K including antiperovskites with Y=H, N, C and O. We used these results to train interpretable machine learning models to understand and further explore this family of compounds. This lead us to predict a further 44 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 anti-perovskites and highlight the importance of the density of states at the Fermi level and of the mass of the Y-atom for the strength of the phonon coupling. Finally, we study in detail a few systems, uncovering some issues with previously published theoretical data. 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.

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:feadc9d12c02ca3decb0034bcf6a9030	2.5 KiB	Description of the contents
LDA_TRAINING.tar.gz MD5md5:74829c4af8d20ff991cf4477e49bcdb3	576.1 MiB	LDA calculations for the training set (4x4x4 q-points) for dynamically stable compounds
LDA_TRAINING_IMAG.tar.gz MD5md5:b99963072e91764a2994286ca84441fe	417.6 MiB	LDA calculations for the training set (4x4x4 q-points) for dynamically unstable compounds
LDA_VALIDATION.tar.gz MD5md5:85ceb509fee1a618e94e1f03b00f05bb	284.4 MiB	LDA calculations for the validation set (4x4x4 q-points) for dynamically stable compounds
LDA_VALIDATION_IMAG.tar.gz MD5md5:f07f1ebc8e9a398ec2e4d63595a44060	1.2 GiB	LDA calculations for the validation set (4x4x4 q-points) for dynamically unstable compounds
LDA_QPT8.tar.gz MD5md5:7c8c4f05e3d414bee86e2e69caf237e1	87.5 MiB	LDA calculations for selected compounds with 8x8x8 q-points
PBE_DONE.tar.gz MD5md5:f80202cdd0bbe526b22f98180ad6324e	82.9 MiB	PBE calculations for selected compounds (4x4x4 q-points) for dynamically stable compounds
PBE_IMAG.tar.gz MD5md5:f27c52252704d4092da1b520753a07e6	36.5 MiB	PBE calculations for selected compounds (4x4x4 q-points) for dynamically unstable compounds
PBE_QPT8.tar.gz MD5md5:5d9e08ee73c05c30f03753b7f590f6a6	47.6 MiB	PBE calculations for selected compounds with 8x8x8 q-points

License

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External references

Keywords

electron-phonon coupling superconductivity anti-perovskites