HTSC-2025：常壓高溫超導體基準數據集——面向人工智能驅動的臨界溫度預測

摘要

高温超導材料的發現對人類工業與日常生活具有重大意義。近年來，利用人工智慧（AI）預測超導轉變溫度的研究日益受到關注，多數此類工具聲稱能達到顯著的準確性。然而，該領域缺乏廣為接受的基準數據集，嚴重阻礙了不同AI算法之間的公平比較，並妨礙了這些方法的進一步發展。在本研究中，我們提出了HTSC-2025，這是一個常壓高溫超導基準數據集。該數據集全面彙編了理論物理學家基於BCS超導理論在2023年至2025年間發現的理論預測超導材料，包括著名的X_2YH_6系統、鈣鈦礦MXH_3系統、M_3XH_8系統、由LaH_{10}結構演化而來的籠狀BCN摻雜金屬原子系統，以及由MgB_2演化而來的二維蜂窩結構系統。HTSC-2025基準已於https://github.com/xqh19970407/HTSC-2025開源，並將持續更新。此基準對於加速基於AI方法的超導材料發現具有重要意義。

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.

HTSC-2025：常壓高溫超導體基準數據集——面向人工智能驅動的臨界溫度預測

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

摘要

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