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 Bardeen Cooper Schrieffer inspired pre-screening 1736 materials high Debye temperature and electronic density states. Next, we perform electron-phonon coupling calculations 1058 them to establish large systematic database BCS superconducting properties. Using McMillan-Allen-Dynes formula, identify 105 dynamically stable transition temperatures, Tc>5 K. Additionally, analyze trends in our dataset individual including MoN, VC, VTe, KB6, Ru3NbC, V3Pt, ScN, LaN2, RuO2, TaC. demonstrate that deep-learning(DL) models can predict superconductor properties faster than direct first principles computations. Notably, find by predicting Eliashberg function as an intermediate quantity, improve model performance versus DL prediction Tc. apply trained on crystallographic open pre-screen candidates further DFT calculations.