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Special Issue:
Featured Column — DATA PAPER
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HTSC-2025: A benchmark dataset of ambient-pressure high-temperature superconductors for AI-driven critical temperature prediction |
| Xiao-Qi Han(韩小琪)1,2, Ze-Feng Gao(高泽峰)1,2,†, Xin-De Wang(王馨德)1,2, Zhenfeng Ouyang(欧阳振峰)1,2, Peng-Jie Guo(郭朋杰)1,2, and Zhong-Yi Lu(卢仲毅)1,2,3 |
1 School of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-nano Devices, Renmin University of China, Beijing 100872, China;
2 Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education), Renmin University of China, Beijing 100872, China;
3 Hefei National Laboratory, Hefei 230088, China |
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Abstract The discovery of high-temperature superconducting materials holds great significance for human industry and daily life. In recent years, research on predicting superconducting transition temperatures using artificial intelligence (AI) has gained popularity, with most of these tools claiming to achieve remarkable accuracy. However, the lack of widely accepted benchmark datasets in this field has severely hindered fair comparisons between different AI algorithms and impeded further advancement of these methods. In this work, we present HTSC-2025, an ambient-pressure high-temperature superconducting benchmark dataset. This comprehensive compilation encompasses theoretically predicted superconducting materials discovered by theoretical physicists from 2023 to 2025 based on BCS superconductivity theory, including the renowned $X_2Y$H$_6$ system, perovskite $MX$H$_3$ system, $M_3X$H$_8$ system, cage-like BCN-doped metal atomic systems derived from LaH$_{10}$ structural evolution, and two-dimensional honeycomb-structured systems evolving from MgB$_2$. In addition, we note a range of approaches inspired by physical intuition for designing high-temperature superconductors, such as hole doping, the introduction of light elements to form strong covalent bonds, and the tuning of spin-orbit coupling. The dataset presented in this paper is openly available at ScienceDB. The HTSC-2025 benchmark has been open-sourced on Hugging Face at https://huggingface.co/datasets/xiao-qi/HTSC-2025 and will be continuously updated, while the Electronic Laboratory for Material Science platform is available at https://in.iphy.ac.cn/eln/link.html#/124/V2s4.
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Received: 10 June 2025
Revised: 14 July 2025
Accepted manuscript online: 16 July 2025
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PACS:
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03.75.Lm
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(Tunneling, Josephson effect, Bose-Einstein condensates in periodic potentials, solitons, vortices, and topological excitations)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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84.35.+i
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(Neural networks)
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05.70.Jk
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(Critical point phenomena)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 62476278, 12434009, and 12204533), the National Key R&D Program of China (Grant No. 2024YFA1408601), and the Innovation Program for Quantum Science and Technology (Grant No. 2021ZD0302402). |
Corresponding Authors:
Ze-Feng Gao
E-mail: zfgao@ruc.edu.cn
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Cite this article:
Xiao-Qi Han(韩小琪), Ze-Feng Gao(高泽峰), Xin-De Wang(王馨德), Zhenfeng Ouyang(欧阳振峰), Peng-Jie Guo(郭朋杰), and Zhong-Yi Lu(卢仲毅) HTSC-2025: A benchmark dataset of ambient-pressure high-temperature superconductors for AI-driven critical temperature prediction 2025 Chin. Phys. B 34 100301
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