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

Area of Science:

• Materials Science
• Condensed Matter Physics
• Computational Chemistry

Background:

• Electronic band structure data offer deeper insights into superconductivity than simpler material properties.
• Density functional theory (DFT) is a powerful computational method for investigating material properties.

Purpose of the Study:

• To generate superconductor lattice structure files optimized for DFT calculations.
• To obtain comprehensive electronic band structure data, including band structures, density of states (DOS), and Fermi surfaces.
• To develop efficient methodologies for data acquisition and extraction from large-scale DFT computations.

Main Methods:

• Utilized DFT to calculate electronic band structures for superconductors.
• Developed high-throughput DFT computational protocols.
• Created tools for extracting electronic band structure data from DFT outputs.

Main Results:

• Generated electronic band structure data for a large number of superconductors.
• Curated a dataset of 1,362 superconductors with transition temperatures (T_c) and 1,112 non-superconducting materials.
• Ensured data quality, accessibility, and usability for machine learning applications.

Conclusions:

• The generated dataset and methodologies facilitate machine learning-driven prediction of superconducting properties.
• This work provides a valuable resource for accelerating the discovery of new superconducting materials.