  We perform a large scale study of conventional superconducting materials
using a machine-learning accelerated high-throughput workflow. We start by
creating a comprehensive dataset of around 7000 electron-phonon calculations
performed with reasonable convergence parameters. This dataset is then used to
train a robust machine learning model capable of predicting the electron-phonon
and superconducting properties based on structural, compositional, and
electronic ground-state properties. Using this machine, we evaluate the
transition temperature (Tc ) of approximately 200000 metallic compounds, all of
which on the convex hull of thermodynamic stability (or close to it) to
maximize the probability of synthesizability. Compounds predicted to have Tc
values exceeding 5 K are further validated using density-functional
perturbation theory. As a result, we identify 545 compounds with Tc values
surpassing 10 K, encompassing a variety of crystal structures and chemical
compositions. This work is complemented with a detailed examination of several
interesting materials, including nitrides, hydrides, and intermetallic
compounds. Particularly noteworthy is LiMoN2 , which we predict to be
superconducting in the stoichiometric trigonal phase, with a Tc exceeding 38 K.
LiMoN2 has been previously synthesized in this phase, further heightening its
potential for practical applications.
