High-pressure phase stability and superconductivity in La-Zr-H hydrides

TL;DR

This study uses computational methods to identify stable and metastable La-Zr-H hydrides at high pressures, predicting their structures and superconducting transition temperatures up to 209 K, and employs machine learning to guide future searches.

Contribution

It provides a comprehensive computational analysis of the La-Zr-H system, discovering new stable phases with high Tc and demonstrating the use of machine learning for predicting superconductivity in hydrides.

Findings

Identified stable superconducting phases with Tc up to 209 K at high pressures.

Discovered a metastable phase with Tc of 206 K just above the convex hull.

Showed that high Tc correlates with high-symmetry, dense hydrogen cage structures.

Abstract

Hydrogen-rich ternary hydrides are promising candidates for high-Tc superconductivity at megabar pressures, yet their chemical space is vast and largely unexplored. Combining evolutionary structure searches with first-principles calculations, we comprehensively investigate the La-Zr-H ternary system in the 150-300 GPa pressure range. Zero-point energy-corrected convex hull analysis identifies multiple stable superconducting phases, including R3m-Zr2H17 at 300 GPa and P6/mmm-LaZr2H24 at 200 GPa, both of which are thermodynamically and dynamically stable and exhibit strong electron-phonon coupling. Solution of the Eliashberg equations predicts high superconducting transition temperatures of Tc = 209 K for R3m-Zr2H17 at 300 GPa and Tc = 202 K for P6/mmm-LaZr2H24 at 200 GPa. In addition to these stable phases, we identify a high-symmetry metastable compound, P6m2-LaZrH18, which lies just 0.027 eV/atom above the convex hull yet remains dynamically stable and exhibits a high predicted Tc of 206 K at 300 GPa. We find that, across all phases, the elevated Tc correlates with the high-symmetry structure with dense hydrogen cages, favorable electron counts per hydrogen, and a large hydrogen-derived density of states at the Fermi level. Finally, a random- forest machine learning model, trained on diverse hydrides superconductivity data, reproduces these structure-property trends across predicted structures, enabling to identify potential hydrides with high predicted Tc for targeted follow-up calculations and future high-pressure experiments.