HTSC-2025：面向AI驱动临界温度预测的常压高温超导体基准数据集

HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction

June 4, 2025

作者: Xiao-Qi Han, Ze-Feng Gao, Xin-De Wang, Zhenfeng Ouyang, Peng-Jie Guo, Zhong-Yi Lu

cs.AI

摘要

高温超导材料的发现对人类工业与日常生活具有重大意义。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，多数工具声称能达到极高的预测精度。然而，该领域缺乏广泛认可的基准数据集，严重阻碍了不同AI算法间的公平比较，也制约了这些方法的进一步发展。本研究提出了HTSC-2025，一个常压高温超导基准数据集。该数据集全面汇集了理论物理学家基于BCS超导理论在2023至2025年间预测发现的超导材料，包括著名的X_2YH_6体系、钙钛矿MXH_3体系、M_3XH_8体系、由LaH_{10}结构演化而来的笼状BCN掺杂金属原子体系，以及从MgB_2演化而来的二维蜂窝结构体系。HTSC-2025基准已开源发布于https://github.com/xqh19970407/HTSC-2025，并将持续更新。此基准对于加速基于AI的超导材料发现具有重要价值。

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_2YH_6 system, perovskite MXH_3 system, M_3XH_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.