Finally, a sampling of predicted Pareto (or near Pareto) optimal materials were selected for experimental validation. Target compositions were also selected to be similar to previously synthesized compounds, yet different enough to challenge the ML-prediction task. In the end, Mg containing compositions were selected for experimental validation (MgAlTiVCr, Mg-TiVNbCr, and MgTiVZrNbHf), even though there are no Mg containing equimolar HEAs in the training data. ML-predictions and experiments are in good agreement, as well as the DFT calculations used to gain insight into mechanisms and thermodynamics of hydrogen absorption in these HEAs. This holistic validation of our data-driven approach to synthesizing predicted Pareto optimal materials serves as a blue print for further hydride discovery, although there remains significant potential for further acceleration. Currently, the speed at which PCT measurements can be performed is a bottleneck to experimental data acquisition and model validation, especially when the kinetics of absorption and desorption are sluggish. Alternative methods could be explored in the future to facilitate faster experimental data acquisition.