Regensburg 2022 – wissenschaftliches Programm
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TT: Fachverband Tiefe Temperaturen
TT 3: Superconductivity: Properties and Electronic Structure
TT 3.7: Vortrag
Montag, 5. September 2022, 11:00–11:15, H23
3DSC - A new dataset of superconductors including crystal structures — •Timo Sommer, Roland Willa, Jörg Schmalian, and Pascal Friederich — Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
More than 100 years after the discovery of superconductivity in mercury, the search for new superconducting materials still remains challenging. In contrast to most other phenomena, making theoretical predictions about the superconductivity of a certain material is extremely difficult, due to the need to model the comparably tiny energy gain of the superconducting phase, compared to the other energy scales of the problem. Data-driven methods, in particular machine learning, are well-known for finding complex patterns in existing datasets. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. Here we present a new and publicly available superconductivity dataset (”3DSC”), featuring the critical temperature Tc of superconducting materials additionally to tested non-superconductors. In contrast to existing databases such as the SuperCon database, the 3DSC contains not only the chemical composition, but also the approximate three-dimensional crystal structure of each material. We perform machine learning experiments which show that access to this structural information improves the prediction of the critical temperature Tc. Additionally, we provide ideas for further research to improve the 3DSC in multiple ways. We argue that expanding and developing the 3DSC is a promising direction towards the reliable prediction of new superconductors using machine learning.