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        <identifier>oai:materialscloud.org:n1mx8-y9r67</identifier>
        <datestamp>2026-08-18T09:00:02Z</datestamp>
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          <dc:contributor>Bispo da Silva, Thalis Henrique</dc:contributor>
          <dc:creator>Bispo da Silva, Thalis Henrique</dc:creator>
          <dc:creator>Wang, Hai-Chen</dc:creator>
          <dc:creator>Tiago Frederico Teixeira, Cerqueira</dc:creator>
          <dc:creator>Di Cataldo, Simone</dc:creator>
          <dc:creator>Botti, Silvana</dc:creator>
          <dc:creator>Marques, Miguel Alexandre Lopes</dc:creator>
          <dc:date>2026-08-18</dc:date>
          <dc:description>&amp;lt;p&amp;gt;Electrical conductivity is a key property for the design of new metallic conductors, but its accurate first-principles prediction is computationally expensive, which has limited its exploration in high-throughput materials screening. In the associated work, we combine machine-learning surrogate models with density-functional theory (DFT) and density-functional perturbation theory (DFPT) to screen approximately 2.8 million inorganic compounds from the Alexandria database for metallic electrical conductivity, using the electron&amp;ndash;phonon coupling constant &amp;lambda; from Eliashberg theory as a proxy for scattering strength. From this pool, only the compounds predicted to exceed a high-conductivity threshold were carried forward for explicit electron&amp;ndash;phonon coupling calculations and Boltzmann transport theory using the EPW code, solving the iterative linearized Boltzmann transport equation (BTE), narrowing the candidates down to 52 top-performing compounds. This dataset provides the DFT, DFPT, and EPW input and output files for these 52 candidates, together with 24 elemental benchmark metals and 3 spin-orbit-coupling reruns computed for validation.&amp;lt;/p&amp;gt;</dc:description>
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          <dc:identifier>https://doi.org/10.24435/materialscloud:4d-hv</dc:identifier>
          <dc:identifier>oai:materialscloud.org:n1mx8-y9r67</dc:identifier>
          <dc:identifier>mcid:2026.161</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:publisher>Materials Cloud</dc:publisher>
          <dc:relation>https://doi.org/10.48550/arXiv.2605.22167</dc:relation>
          <dc:relation>https://archive.materialscloud.org/communities/mcarchive</dc:relation>
          <dc:relation>https://doi.org/10.24435/materialscloud:4z-4m</dc:relation>
          <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
          <dc:rights>Creative Commons Zero v1.0 Universal</dc:rights>
          <dc:rights>https://creativecommons.org/publicdomain/zero/1.0/legalcode</dc:rights>
          <dc:subject>Quantum ESPRESSO</dc:subject>
          <dc:subject>EPW</dc:subject>
          <dc:subject>Eletrical Conductivity</dc:subject>
          <dc:subject>High-Throughput</dc:subject>
          <dc:subject>Machine Learning</dc:subject>
          <dc:title>High-throughput study of electrical conductivity in ordered metals</dc:title>
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