Composition-Based Machine Learning for Screening Superconducting Ternary Hydrides from a Curated Dataset

TL;DR

This paper introduces an ensemble machine-learning method to efficiently screen and identify promising superconducting ternary hydrides at high pressures, leveraging composition data and feature importance analysis.

Contribution

The study develops a novel ensemble XGBoost model trained on curated hydride data to predict and identify high-potential superconducting compositions without relying on structural information.

Findings

Identified promising high-pressure hydride compositions like Ca-Ti-H, Li-K-H, and Na-Mg-H.

Demonstrated the effectiveness of ensemble models in screening large compositional spaces.

Highlighted elemental properties influencing superconducting transition temperatures.

Abstract

We present an ensemble machine-learning approach for composition-based, structure-agnostic screening of 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 approximately 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.