SuperBand: Superconductor's energy band

In comparison to simpler data such as chemical formulas and lattice structures, energy 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 key electronic properties for experimentally confirmed superconductors, including band structures, density of states (DOS), and Fermi surface data. Additionally, we outline efficient methodologies for acquiring structural 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 2474 superconductors along with their experimentally determined superconducting transition temperatures (Tc), which is well-suited for machine learning applications. This work also provides guidelines for accessing and utilizing this dataset. Furthermore, we present a neural network model designed for training with this data. All the aforementioned data and code are publicly available at http://www.superband.work.

相较于化学式与晶格结构等基础数据形式，能带结构数据可对超导现象提供更为本质且直观的认知。本研究针对密度泛函理论（Density Functional Theory, DFT）计算优化生成了超导体晶格结构文件；通过DFT计算，我们为经实验证实的超导体获取了关键电子性质数据，涵盖能带结构、态密度（Density of States, DOS）与费米面数据。此外，本研究梳理了结构数据的高效获取方法，构建了高通量DFT计算流程，并开发了可从大规模DFT计算中提取目标数据的工具。作为示例，我们整理了包含2474种超导体信息及其实验测得的超导转变温度（Tc）的数据集，该数据集适配各类机器学习应用场景。本研究同时提供了该数据集的访问与使用指南；此外，我们还提出了专为该数据集训练设计的神经网络模型。上述所有数据与代码均可在http://www.superband.work公开获取。

搜集汇总

背景与挑战

背景概述

该数据集提供了超导体的能带结构、态密度和费米面等关键电子特性数据，包含2474种超导体的信息及其超导转变温度，适用于机器学习研究。数据集还提供了获取和处理这些数据的方法和工具。