HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction
xiao-qi
Abstract
HTSC-2025, a benchmark dataset for high-temperature superconducting materials, is presented to facilitate AI-based discovery in this field.
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.
Community
We introduce HTSC-2025, a benchmark dataset of ambient-pressure high-temperature superconductors designed to advance AI-driven prediction of superconducting transition temperatures. The dataset includes materials predicted between 2023 and 2025 based on BCS theory, such as X₂YH₆, MXH₃, M₃XH₈, BCN-doped LaH₁₀-derived systems, and 2D MgB₂-like structures. It also reflects design strategies inspired by physical intuition, including hole doping, light-element covalent bonding, and spin–orbit coupling tuning. HTSC-2025 is open-sourced at https://github.com/xqh19970407/HTSC-2025 and will be continuously updated to support fair benchmarking and accelerate AI-driven superconductor discovery.
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