ES22-Choudhary

Author: Choudhary, Kamal - National Institute of Standards and Technology, Gaithersburg, MD, USA

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

Abstract: Recent advances in first principles calculations and machine learning techniques allow for 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 988 of them to establish a large and systematic database of BCS superconducting properties. Using the McMillan-Allen-Dynes formula, we identify 112 dynamically stable materials with transition temperatures, Tc > 5 K. In addition, we analyze trends in our dataset and individual materials including MoN, VC, VTe, KB6, Ru3NbC, V3Pt, ScN, LaN2, RuO2, and TaC. Finally, we demonstrate that deep-learning models 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.

Other authors: Garrity, Kevin, National Institute of Standards and Technology, Gaithersburg, Maryland, USA