HTSC-2025 is a publicly released benchmark dataset aggregating theoretically predicted ambient-pressure high-Tc superconductors from recent BCS-based studies for training and comparing AI models that predict critical temperature.
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An ensemble of XGBoost regression models trained on ~2000 hydrides screens ternary A-B-H compositions for high-Tc superconductivity at 100-300 GPa and flags promising systems such as Ca-Ti-H, Li-K-H, and Na-Mg-H.
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HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction
HTSC-2025 is a publicly released benchmark dataset aggregating theoretically predicted ambient-pressure high-Tc superconductors from recent BCS-based studies for training and comparing AI models that predict critical temperature.
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Composition-Based Machine Learning for Screening Superconducting Ternary Hydrides from a Curated Dataset
An ensemble of XGBoost regression models trained on ~2000 hydrides screens ternary A-B-H compositions for high-Tc superconductivity at 100-300 GPa and flags promising systems such as Ca-Ti-H, Li-K-H, and Na-Mg-H.