Machine learning driven exploration of hydride superconductors at ambient pressure

Hydrogen-rich materials are among the most promising candidates for achieving high-temperature superconductivity due to their light atomic mass and strong phonon-mediated Cooper pairing. While many high-temperature superconducting hydrides have been reported experimentally, their practical applicability remains limited due to the required extreme conditions, motivating the search for stable or metastable superconducting phases at ambient pressure. Here, we combine ab initio electron–phonon coupling calculations with machine learning methods to explore over two million hydride structures, identifying more than 600 compounds with predicted critical temperatures ( T c ) above 20 K. This dataset reveals a large chemical and structural diversity among potential superconductors, including perovskites, kagome lattices, and compounds with isolated hydrogen octahedra, among others. Despite this diversity, high- T c materials consistently exhibit substantial hydrogen contributions to the electronic density of states at the Fermi level. Symbolic regression analysis quantitatively confirms this correlation. Thermodynamic analysis shows that all identified superconductors lie above the convex hull of stability, with energies typically exceeding 100 meV/atom above the hull. This implies that experimental synthesis can only be obtained through non-equilibrium approaches, including high-pressure or high-temperature methods followed by quenching, or by thin-film deposition techniques. Moreover, as many promising candidates resemble degenerate semiconductors, a possible synthesis route may involve controlled doping strategies of parent semiconducting compounds. This work substantially expands the known landscape of hydride superconductors and establishes design principles to guide future experimental realization.