Designing high-TC superconductors with BCS-inspired screening, density functional theory, and deep-learning

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. Next, we perform electron-phonon coupling calculations for 1058 of them to establish a large and systematic database of BCS superconducting properties. Using the McMillan-Allen-Dynes formula, we identify 105 dynamically stable materials with transition temperatures, T-C >= 5 K. Additionally, we analyze trends in our dataset and individual materials including MoN, VC, VTe, KB6, Ru3NbC, V3Pt, ScN, LaN2, RuO2, and TaC. We demonstrate that deep-learning(DL) models can predict superconductor properties faster than direct first-principles computations. Notably, we find that by predicting the Eliashberg function as an intermediate quantity, we can improve model performance versus a direct DL prediction of T-C. We apply the trained models on the crystallographic open database and pre-screen candidates for further DFT calculations.

Automated summary

We developed a multi-step workflow to discover conventional superconductors and established a large database. We identified 105 candidate materials with stability using advanced computational methods and analyzed trends in the dataset and individual materials. Furthermore, deep learning models can predict superconductor properties faster, and performance can be improved by predicting an intermediate quantity.