About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Discovery and Design of Emerging Materials
|
Presentation Title |
Data-driven Discovery of the Functional Form of the Superconducting Critical Temperature |
Author(s) |
Stephen Raymond Xie, Gregory R. Stewart, James J Hamlin, Peter J. Hirschfeld, Richard G. Hennig |
On-Site Speaker (Planned) |
Peter J. Hirschfeld |
Abstract Scope |
Predicting the critical temperature Tc of superconductors is a notoriously difficult task, even for electron-phonon systems. We build on earlier efforts by McMillan and Allen and Dynes to model Tc from various measures of the phonon spectrum and the electron-phonon interaction by using machine learning algorithms. Specifically, we use the Sure Independence and Sparsifying Operator (SISSO) method to identify a new, physically interpretable equation for Tc as a function of a small number of physical quantities. We show that our model, trained using the relatively small Tc < 10K data tested by Allen and Dynes, improves upon the Allen-Dynes fit and can reasonably generalize to superconducting materials with higher Tc such as H3S. By incorporating physical insights and constraints into a data-driven approach, we demonstrate that machine-learning methods can identify the relevant physical quantities and obtain predictive equations using small but high-quality datasets. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |