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Abstract

A large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow is performed, starting 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, the transition temperatures (T_c) of approximately 200 000 metallic compounds are evaluated, all of which are on the convex hull of thermodynamic stability (or close to it) to maximize the probability of synthesizability. Compounds predicted to have T_c values exceeding 5 K are further validated using density-functional perturbation theory. As a result, 541 compounds with T_c values surpassing 10 K, encompassing a variety of crystal structures and chemical compositions, are identified. This work is complemented with a detailed examination of several interesting materials, including nitrides, hydrides, and intermetallic compounds. Particularly noteworthy is LiMoN₂, which is predicted to be superconducting in the stoichiometric trigonal phase, with a T_c exceeding 38 K. LiMoN₂ has previously been synthesized in this phase, further heightening its potential for practical applications.