Critical Temperature Prediction of Superconductors Based on Machine Learning: A Short Review

Juntao Jiang, Renjun Xu

Keywords: Superconductors, machine learning, material discovery, critical temperature prediction

Abstract: Superconductors have a lot of promising potential applications in power transmission and power magnet development because of their special characteristics. However, new superconductor discovery requires extensive trial-and-error experimentation, which is time-consuming and expensive. The development of machine learning techniques makes it possible for identifying superconductors and predicting their critical temperature from the material's proprieties. This paper gives a short review of machine learning's applications in superconductors' critical temperature prediction. Related datasets and different proposed methods are included. And we also discussed the future research directions and opportunities in this field.