Machine learning conventional superconductors
11th. Dec. 2023, Seminar, HZO 20, Universitätsstr. 150, 44801 Bochum
Time:
Start: 11th. Dec. 2023. 12:00 a.m.
End: 11th. Dec. 2023. 01:00 p.m.
Author(s):
Prof. Dr. Miguel Marques RC-FEMS, Ruhr-Universität Bochum
Abstract:
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.