Upper limit of the transition temperature of superconducting materials

Yang Liu; Haiyou Huang; Jie Yuan; Yan Zhang; Hongyuan Feng; Ning Chen; Yang Li; Jiao Teng; Kui Jin; Dezhen Xue; Yanjing Su

doi:10.1016/j.patter.2022.100609

Upper limit of the transition temperature of superconducting materials

Patterns (N Y). 2022 Oct 24;3(11):100609. doi: 10.1016/j.patter.2022.100609. eCollection 2022 Nov 11.

Authors

Yang Liu^{1

2

3}, Haiyou Huang^{1

2}, Jie Yuan³, Yan Zhang^{1

2}, Hongyuan Feng^{1

2}, Ning Chen⁴, Yang Li⁵, Jiao Teng⁴, Kui Jin³, Dezhen Xue⁶, Yanjing Su^{1

2}

Affiliations

¹ Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China.
² Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China.
³ Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China.
⁴ School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China.
⁵ Department of Engineering Science and Materials, University of Puerto Rico, Mayaguez, PR 00681-9000, USA.
⁶ State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China.

Abstract

Why are the transition temperatures (T _c) of superconducting materials so different? The answer to this question is not only of great significance in revealing the mechanism of high-T _c superconductivity but also can be used as a guide for the design of new superconductors. However, so far, it is still challenging to identify the governing factors affecting the T _c. In this work, with the aid of machine learning and first-principles calculations, we found a close relevance between the upper limit of the T _c and the energy-level distribution of valence electrons. It implies that some additional inter-orbital electron-electron interaction should be considered in the interpretation of high-T _c superconductivity.

Keywords: first-principles calculation; high temperature superconductors; machine learning; materials informatics; superconductivity; transition temperature.