Al-Based Few-Hydrogen Metal-Bonded Perovskite High-$T_c$ Superconductor Al${}_4$H up to 54 K under Atmospheric Pressure

Abstract

Multi-hydrogen lanthanum hydrides have shown the highest critical temperature $T_c$ at 250-260 K under 170-200 GPa. However, such high pressure is a great challenge for sample preparation and practical application. To address this challenge, we propose a novel design strategy for ambient-pressure high-$T_c$ superconductors by constructing new few-hydrogen metal-bonded perovskite hydrides, such as Al-based superconductor $\mathrm{A}{\mathrm{l}}_4\mathrm{H}$, with better ductility than the well-known multi-hydrogen, cuprate and Fe-based superconductors. Based on the Migdal-Eliashberg theory, we predict that the structurally stable $\mathrm{A}{\mathrm{l}}_4\mathrm{H}$ has a favorable high $T_c$ up to 54 K under atmospheric pressure, similar to SmOFeAs.

6 pages, 4 figures

References in corpus (9)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• Advanced capabilities for materials modelling with Quantum ESPRESSO
• Quantum ESPRESSO toward the exascale
• Hydrogen sulphide at high pressure: a strongly-anharmonic phonon-mediated superconductor
• Phonon-mediated high-temperature superconductivity in ternary borohydride KB${}_2$H${}_8$ around 12 GPa
• Algorithm for generating irreducible site-occupancy configurations
• Strong Anharmonic and Quantum Effects in Pm-3n AlH3 Under High Pressure: A First-Principles Study

Cited by in corpus (4)

• Prediction of Ambient Pressure Conventional Superconductivity above 80K in Thermodynamically Stable Hydride Compounds
• The Maximum $T_c$ of Conventional Superconductors at Ambient Pressure
• Search for thermodynamically stable ambient-pressure superconducting hydrides in GNoME database
• HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction