超带:超导体电子带和费米表面结构数据库

Tengdong Zhang, Chenyu Suo, Yanling Wu, Xiaodan Xu, Yong Liu, Dao-Xin Yao, Jun Li
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引用次数: 0

摘要

与化学式和晶格结构等更简单的数据相比,电子能带结构数据为超导现象提供了更基本、更直观的洞察力。在这项工作中,我们生成了为密度泛函理论(DFT)计算而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带,包括带结构、状态密度(DOS)和费米面数据。此外,我们还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 计算中提取这些数据的工具。举例来说,我们整理了一个数据集,其中包含 2474 种超导体的信息以及实验测定的超导转变温度,非常适合机器学习应用。这项工作还提供了访问和使用该数据集的指南。此外,我们还提出了一个神经网络模型,旨在利用这些数据进行训练。所有上述数据和代码均可在 http://www.superband.work 公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Superband: an Electronic-band and Fermi surface structure database of superconductors
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 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 2474 superconductors along with their experimentally determined superconducting transition temperatures, 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.
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