构建首个常压高温超导体基准数据集，助力AI预测临界温度
HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction
- 基于BCS理论构建2023-2025年理论预测的超导材料数据集
- 涵盖多种结构体系，包括X₂YH₆、MXH₃等典型系统
- 开源可更新，适合超导材料发现与AI算法评估研究者使用
高温超导材料的发现对人类工业和日常生活具有重要意义。近年来，利用人工智能（AI）预测超导转变温度的研究日益流行，但该领域缺乏广泛接受的基准数据集，严重阻碍了不同AI算法的公平比较及方法的进一步发展。本文提出HTSC-2025，一个常压高温超导体基准数据集。该数据集综合了2023至2025年间理论物理学家基于BCS超导理论预测的超导材料，包括著名的X₂YH₆体系、钙钛矿型MXH₃体系、M₃XH₈体系、源自LaH₁₀结构演化的笼状BCN掺杂金属原子体系，以及由MgB₂演化而来的二维蜂窝结构体系。HTSC-2025已开源并持续更新，网址为https://github.com/xqh19970407/HTSC-2025。该基准数据集对加速基于AI的超导材料发现具有重要意义。
原文摘要 · Abstract (English)
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 the 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$_2$YH$_6$ system, perovskite MXH$_3$ system, M$_3$XH$_8$ system, cage-like BCN-doped metal atomic systems derived from LaH$_{10}$ structural evolution, and two-dimensional honeycomb-structured systems evolving from MgB$_2$. The HTSC-2025 benchmark has been open-sourced at https://github.com/xqh19970407/HTSC-2025 and will be continuously updated. This benchmark holds significant importance for accelerating the discovery of superconducting materials using AI-based methods.
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