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<p>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 (<i>T</i><sub><i>c</i></sub>) 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.</p>

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SuperBand: an Electronic-band and Fermi surface structure database of superconductors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月6日

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  • Tengdong Zhang,
  • Chenyu Suo,
  • Yanling Wu,
  • Xiaodan Xu,
  • Yong Liu,
  • Dao-Xin Yao,
  • Jun Li

摘要

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 (Tc) 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.