Cuprate superconducting materials above liquid nitrogen temperature from machine learning-Reference-Cited by-同舟云学术

Cuprate superconducting materials above liquid nitrogen temperature from machine learning

Published:2023 Issue:29 Volume:13 Page:19836-19845
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Wang Yuxue¹²³^ORCID,Su Tianhao¹²³^ORCID,Cui Yaning¹²³,Ma Xianzhe¹²³,Zhou Xue⁴,Wang Yin¹²³^ORCID,Hu Shunbo¹²³^ORCID,Ren Wei¹²³^ORCID

Affiliation:

1. Department of Physics, Material Genome Institute, Institute for the Conservation of Cultural Heritage, Shanghai University, Shanghai 200444, China

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

A knowledge-based machine learning system predicts the superconducting critical temperature of materials. The key descriptors of the model obtained by SHAP analysis are consistent with physical cognition.

Funder

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

State Key Laboratory of Advanced Special Steel

Key Research and Development Projects of Shaanxi Province

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/RA/D3RA02848H

Reference77 articles.

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