An iterative scheme using foundation models and SSCHA enables efficient crystal structure prediction with anharmonic effects, shown to match DFT benchmarks on the H3S system from 50 to 200 GPa.
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HPCSD compiles 77,346 pressure-resolved crystal structures for 89 elements with consistent DFT re-optimization and analysis showing ubiquitous pressure-induced polymorphism with family-dependent trends.
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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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.