Machine learning conventional superconductors

11th. Dec. 2023, Seminar, HZO 20, Universitätsstr. 150, 44801 Bochum

Start: 11th. Dec. 2023. 12:00 a.m.
End: 11th. Dec. 2023. 01:00 p.m.

Prof. Dr. Miguel Marques RC-FEMS, Ruhr-Universität Bochum

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.

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