Superconductivity in antiperovskites


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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Hoffmann, Noah</dc:creator>
  <dc:creator>F. T. Cerqueira, Tiago</dc:creator>
  <dc:creator>Schmidt, Jonathan</dc:creator>
  <dc:creator>L. Marques, Miguel A.</dc:creator>
  <dc:date>2022-04-06</dc:date>
  <dc: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.</dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2022.49</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:6g-az</dc:identifier>
  <dc:identifier>mcid:2022.49</dc:identifier>
  <dc:identifier>oai:materialscloud.org:1311</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>electron-phonon coupling</dc:subject>
  <dc:subject>superconductivity</dc:subject>
  <dc:subject>anti-perovskites</dc:subject>
  <dc:title>Superconductivity in antiperovskites</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>