Cuprate superconducting materials above liquid nitrogen temperature from machine learning

Yuxue Wang; Tianhao Su; Yaning Cui; Xianzhe Ma; Xue Zhou; Yin Wang; Shunbo Hu; Wei Ren

doi:10.1039/d3ra02848h

Cuprate superconducting materials above liquid nitrogen temperature from machine learning

RSC Adv. 2023 Jul 3;13(29):19836-19845. doi: 10.1039/d3ra02848h. eCollection 2023 Jun 29.

Authors

Yuxue Wang^{1

2

3}, Tianhao Su^{1

2

3}, Yaning Cui^{1

2

3}, Xianzhe Ma^{1

2

3}, Xue Zhou⁴, Yin Wang^{1

2

3}, Shunbo Hu^{1

2

3}, Wei Ren^{1

2

3}

Affiliations

¹ Department of Physics, Material Genome Institute, Institute for the Conservation of Cultural Heritage, Shanghai University Shanghai 200444 China renwei@shu.edu.cn shunbohu@shu.edu.cn.
² Shanghai Key Laboratory of High Temperature Superconductors, International Center for Quantum and Molecular Structures, Shanghai University Shanghai 200444 China.
³ Zhejiang Lab Hangzhou 311100 China.
⁴ Center for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University Xi'an Shaanxi 710049 China.

Abstract

The superconductivity of cuprates remains a challenging topic in condensed matter physics, and the search for materials that superconduct electricity above liquid nitrogen temperature and even at room temperature is of great significance for future applications. Nowadays, with the advent of artificial intelligence, research approaches based on data science have achieved excellent results in material exploration. We investigated machine learning (ML) models by employing separately the element symbolic descriptor atomic feature set 1 (AFS-1) and a prior physics knowledge descriptor atomic feature set 2 (AFS-2). An analysis of the manifold in the hidden layer of the deep neural network (DNN) showed that cuprates still offer the greatest potential as superconducting candidates. By calculating the SHapley Additive exPlanations (SHAP) value, it is evident that the covalent bond length and hole doping concentration emerge as the crucial factors influencing the superconducting critical temperature (T_c). These findings align with our current understanding of the subject, emphasizing the significance of these specific physical quantities. In order to improve the robustness and practicability of our model, two types of descriptors were used to train the DNN. We also proposed the idea of cost-sensitive learning, predicted the sample in another dataset, and designed a virtual high-throughput search workflow.