title: HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature
    Superconductors for AI-Driven Critical Temperature Prediction
summary: 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.
paper_authors._id: 68418b4fa88edf9e4b783062
paper_authors.name: Xiao-Qi Han
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T12:41:46.123Z
paper_authors._id: 68416c7e2deb112bf3c0f0d7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/6wcC033AOagYXs39FIWUy.png
paper_authors.fullname: xiao-qi han
paper_authors.type: user
paper_authors.user: xiao-qi
paper_authors._id: 68418b4fa88edf9e4b783063
paper_authors.name: Ze-Feng Gao
paper_authors._id: 68418b4fa88edf9e4b783064
paper_authors.name: Xin-De Wang
paper_authors._id: 68418b4fa88edf9e4b783065
paper_authors.name: Zhenfeng Ouyang
paper_authors._id: 68418b4fa88edf9e4b783066
paper_authors.name: Peng-Jie Guo
paper_authors._id: 68418b4fa88edf9e4b783067
paper_authors.name: Zhong-Yi Lu
paper_title: HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature
    Superconductors for AI-Driven Critical Temperature Prediction
paper_summary: 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.
paper_ai_summary: HTSC-2025, a benchmark dataset for high-temperature superconducting materials, is presented to facilitate AI-based discovery in this field.