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Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning

Published

Author(s)

Kamal Choudhary, Kevin Garrity

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

Citation

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 April 14, 2024)
Created February 13, 2023