  The observation of superconductivity in hydride-based materials under
ultrahigh pressures (for example, H$_3$S and LaH$_{10}$) has fueled the
interest in a more data-driven approach to discovering new high-pressure
hydride superconductors. In this work, we performed density functional theory
(DFT) calculations to predict the critical temperature ($T_c$) of over 900
hydride materials under a pressure range of (0 to 500) GPa, where we found 122
dynamically stable structures with a $T_c$ above MgB$_2$ (39 K). To accelerate
screening, we trained a graph neural network (GNN) model to predict $T_c$ and
demonstrated that a universal machine learned force-field can be used to relax
hydride structures under arbitrary pressures, with significantly reduced cost.
By combining DFT and GNNs, we can establish a more complete map of hydrides
under pressure.
