  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, Tc>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 Tc. We apply the trained
models on the crystallographic open database and pre-screen candidates for
further DFT calculations.
