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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An adapted U-Net model trained on mean-field phase diagrams accurately predicts Hamiltonian parameters for a cuprate superconductor when validated on Monte Carlo simulation data.
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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.
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Predicting parameters of a model cuprate superconductor using machine learning
An adapted U-Net model trained on mean-field phase diagrams accurately predicts Hamiltonian parameters for a cuprate superconductor when validated on Monte Carlo simulation data.