Choudhary, K.
and Garrity, K.
(2023),
Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning, Physical Review B, [online], https://doi.org/10.1038/s41524-022-00933-1, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934734
(Accessed October 27, 2025)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning
Published
Author(s)
,
Abstract
Recent advances in first principles calculations and machine learning techniques allow a systematic search for phonon-mediated superconductors. We develop a multi-step workflow for the discovery of conventional superconductors, starting with a Bardeen–Cooper–Schrieffer inspired pre-screening of 1736 materials with high Debye temperature and electronic density of states at the Fermi-level. Next, we perform electron-phonon coupling calculations for 620 of them. Using the McMillan-Allen-Dynes formula, we identify 83 dynamically stable materials with transition temperatures >5K. In addition, we analyze trends in our dataset and individual materials including MoN, VC, VTe, KB6 and TaC. Finally, we develop deep-learning models that can predict superconductor properties, including the Eliashberg function, thousands of times faster than direct first principles computations. We apply the trained model on the crystallographic open database and pre-screen 8293 candidates for further DFT calculations.Citation
Physical Review B
Pub Type
Journals
Download Paper
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact [email protected].
Created February 13, 2023