SuperBand: an Electronic-band and Fermi surface structure database of superconductors
Abstract In comparison to simpler data such as chemical formulas and lattice structures, electronic band structure data provide a more fundamental and intuitive insight into superconducting phenomena. In this work, we generate superconductor’s lattice structure files optimized for density functional...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05015-7 |
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| author | Tengdong Zhang Chenyu Suo Yanling Wu Xiaodan Xu Yong Liu Dao-Xin Yao Jun Li |
| author_facet | Tengdong Zhang Chenyu Suo Yanling Wu Xiaodan Xu Yong Liu Dao-Xin Yao Jun Li |
| author_sort | Tengdong Zhang |
| collection | DOAJ |
| description | Abstract In comparison to simpler data such as chemical formulas and lattice structures, electronic band structure data provide a more fundamental and intuitive insight into superconducting phenomena. In this work, we generate superconductor’s lattice structure files optimized for density functional theory (DFT) calculations. Through DFT, we obtain electronic band for superconductors, including band structures, density of states (DOS), and Fermi surface data. Additionally, we outline efficient methodologies for acquiring structure data, establish high-throughput DFT computational protocols, and introduce tools for extracting this data from large-scale DFT calculations. As an example, we have curated a dataset containing information on 1,362 superconductors along with their experimentally determined superconducting transition temperatures (T c ) as well as 1,112 experimentally verified non-superconducting materials, which is well-suited for machine learning applications. This dataset is constructed with a focus on data quality, accessibility, and usability for machine learning models aimed at predicting superconducting properties. |
| format | Article |
| id | doaj-art-dfef72b9b74b450c80df6902c5f93c83 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-dfef72b9b74b450c80df6902c5f93c832025-08-20T03:09:34ZengNature PortfolioScientific Data2052-44632025-05-011211910.1038/s41597-025-05015-7SuperBand: an Electronic-band and Fermi surface structure database of superconductorsTengdong Zhang0Chenyu Suo1Yanling Wu2Xiaodan Xu3Yong Liu4Dao-Xin Yao5Jun Li6State Key Laboratory of Metastable Materials Science and Technology, Hebei Key Laboratory of Microstructural Material Physics, School of Science, Yanshan UniversityState Key Laboratory of Metastable Materials Science and Technology, Hebei Key Laboratory of Microstructural Material Physics, School of Science, Yanshan UniversityState Key Laboratory of Metastable Materials Science and Technology, Hebei Key Laboratory of Microstructural Material Physics, School of Science, Yanshan UniversityState Key Laboratory of Metastable Materials Science and Technology, Hebei Key Laboratory of Microstructural Material Physics, School of Science, Yanshan UniversityState Key Laboratory of Metastable Materials Science and Technology, Hebei Key Laboratory of Microstructural Material Physics, School of Science, Yanshan UniversityState Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen UniversityState Key Laboratory of Metastable Materials Science and Technology, Hebei Key Laboratory of Microstructural Material Physics, School of Science, Yanshan UniversityAbstract In comparison to simpler data such as chemical formulas and lattice structures, electronic band structure data provide a more fundamental and intuitive insight into superconducting phenomena. In this work, we generate superconductor’s lattice structure files optimized for density functional theory (DFT) calculations. Through DFT, we obtain electronic band for superconductors, including band structures, density of states (DOS), and Fermi surface data. Additionally, we outline efficient methodologies for acquiring structure data, establish high-throughput DFT computational protocols, and introduce tools for extracting this data from large-scale DFT calculations. As an example, we have curated a dataset containing information on 1,362 superconductors along with their experimentally determined superconducting transition temperatures (T c ) as well as 1,112 experimentally verified non-superconducting materials, which is well-suited for machine learning applications. This dataset is constructed with a focus on data quality, accessibility, and usability for machine learning models aimed at predicting superconducting properties.https://doi.org/10.1038/s41597-025-05015-7 |
| spellingShingle | Tengdong Zhang Chenyu Suo Yanling Wu Xiaodan Xu Yong Liu Dao-Xin Yao Jun Li SuperBand: an Electronic-band and Fermi surface structure database of superconductors Scientific Data |
| title | SuperBand: an Electronic-band and Fermi surface structure database of superconductors |
| title_full | SuperBand: an Electronic-band and Fermi surface structure database of superconductors |
| title_fullStr | SuperBand: an Electronic-band and Fermi surface structure database of superconductors |
| title_full_unstemmed | SuperBand: an Electronic-band and Fermi surface structure database of superconductors |
| title_short | SuperBand: an Electronic-band and Fermi surface structure database of superconductors |
| title_sort | superband an electronic band and fermi surface structure database of superconductors |
| url | https://doi.org/10.1038/s41597-025-05015-7 |
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