HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature
  Superconductors for AI-Driven Critical Temperature Prediction
Authors: name: Xiao-Qi Han, name: Ze-Feng Gao, name: Xin-De Wang, name: Zhenfeng Ouyang, name: Peng-Jie Guo, name: Zhong-Yi Lu

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.