Zhang Tengdong, Suo Chenyu, Wu Yanling, Xu Xiaodan, Liu Yong, Yao Dao-Xin, Li Jun
State Key Laboratory of Metastable Materials Science and Technology, Hebei Key Laboratory of Microstructural Material Physics, School of Science, Yanshan University, Qinhuangdao, 066004, China.
State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou, 510275, China.
Sci Data. 2025 May 6;12(1):744. doi: 10.1038/s41597-025-05015-7.
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) 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.
与诸如化学式和晶格结构等更简单的数据相比，电子能带结构数据能为超导现象提供更基础、更直观的见解。在这项工作中，我们生成了针对密度泛函理论（DFT）计算进行优化的超导体晶格结构文件。通过DFT，我们获得了超导体的电子能带，包括能带结构、态密度（DOS）和费米面数据。此外，我们概述了获取结构数据的有效方法，建立了高通量DFT计算协议，并介绍了从大规模DFT计算中提取此数据的工具。例如，我们整理了一个数据集，其中包含1362种超导体及其通过实验确定的超导转变温度（T）的信息，以及1112种经实验验证的非超导材料，该数据集非常适合机器学习应用。构建此数据集时重点关注了针对旨在预测超导特性的机器学习模型的数据质量、可访问性和可用性。
