We present an ensemble machine-learning approach for composition-based screening without explicit structural information for candidate superconductors among ternary hydrides under high pressure. Hydrogen-rich hydrides are known to exhibit high superconducting transition temperatures, and ternary or multinary hydrides can stabilize superconducting phases at reduced pressures through chemical compression. To systematically explore this vast compositional space, we construct an ensemble of 30 XGBoost regression models trained on a curated dataset of ∼2000 binary and ternary hydride entries. The model ensemble is used to screen a broad set of A–B–H compositions at pressures of 100, 200, and 300 GPa, with screening outcomes evaluated statistically based on prediction consistency across ensemble members. This analysis highlights several high-scoring compositional systems, including Ca–Ti–H, Li–K–H, and Na–Mg–H, which were not explicitly included in the training dataset. In addition, feature-importance analysis indicates that elemental properties, such as ionization energy and atomic radius, contribute significantly to the learned composition-level trends in superconducting transition temperature. Overall, these results demonstrate the utility of ensemble-based machine learning as a primary screening tool for identifying promising regions of chemical space in superconducting hydrides.
Tokuyama et al. (2026) studied this question.