Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors
文献类型：期刊论文
| 作者 | Zhao, Jinbin1,2; Wang, Jiantao2,3; He, Dongchang2,3; Li, Junlin1; Sun, Yan2; Chen, Xing-Qiu2; Liu, Peitao2 |
| 刊名 | ACTA METALLURGICA SINICA
 |
| 出版日期 | 2024-10-01 |
| 卷号 | 60期号:10页码:1418-1428 |
| 关键词 | hydride superconductor superconducting transition temperature machine learning random forest first-principles calculation |
| ISSN号 | 0412-1961 |
| DOI | 10.11900/0412.1961.2024.00140 |
| 通讯作者 | Chen, Xing-Qiu(xingqiu.chen@imr.ac.cn) ; Liu, Peitao(ptliu@imr.ac.cn) |
| 英文摘要 | The discovery of hydride superconductors with high critical transition temperature (T-c) un der high pressures has received considerable interest in developing superconducting materials that can operate at room temperature and ambient pressure. Although first-principles methods can accurately predict the critical temperature of hydride superconductors, the computational demands are significant because of the expensive calculation of electron- phonon coupling. Hence, constructing an accurate and efficient model for predicting T-c is highly desirable. In this study, a simple and interpretable machine learning (ML) model was developed using the random forest algorithm, which enables the selection of important features based on their importance. Using four physics-based features, namely, the standard deviation of the number of valence electrons, mean covalent radii, range of the Mendeleev number of constituent elements, and hydrogen fraction of the total density of states at the Fermi energy, the optimal ML model achieves high accuracy, with a mean absolute error of 24.3 K and a root-mean-square error of 33.6 K. The ML model developed in this study shows great application potential for high-throughput screening, thereby expediting the discovery of high-T-c superconducting hydrides. |
| 资助项目 | National Natural Science Foundation of China[52188101] ; National Natural Science Foundation of China[52201030] ; National Key Research and Development Program of China[2021YFB3501503] ; Key Research Program of Chinese Academy of Sciences |
| WOS研究方向 | Metallurgy & Metallurgical Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001334426200010 |
| 出版者 | SCIENCE PRESS |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; Key Research Program of Chinese Academy of Sciences |
| 源URL |  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Chen, Xing-Qiu; Liu, Peitao |
| 作者单位 | 1.Taiyuan Univ Sci & Technol, Sch Mat Sci & Engn, Taiyuan 030024, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China 3.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Jinbin,Wang, Jiantao,He, Dongchang,et al. Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors[J]. ACTA METALLURGICA SINICA,2024,60(10):1418-1428. |
| APA | Zhao, Jinbin.,Wang, Jiantao.,He, Dongchang.,Li, Junlin.,Sun, Yan.,...&Liu, Peitao.(2024).Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors.ACTA METALLURGICA SINICA,60(10),1418-1428. |
| MLA | Zhao, Jinbin,et al."Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors".ACTA METALLURGICA SINICA 60.10(2024):1418-1428. |
入库方式： OAI收割
来源：金属研究所
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