) = 20944 possible compositions. Taking the hydriding element subset of H = {Mg, Ti, V, Zr, Nb, Pd, Hf, Ta}, we then remove any composition whose combined molar fraction of H elements is less than 0.25 (since there are no experimental measurements in the ML-HydPARK dataset on compositions that only contain non-hydriding elements). This reduces screening space to a total of 17,920 unique equimolar compositions. The predicted log equilibrium plateau pressures, plotted as a function of the most important ML feature, νpa , span many orders of magnitude across the HEA screening set (Figure ). This emphasizes the attractiveness of HEAs as a materials platform for hydrogen applications since they generally support continuous tuning of properties via expansive possibilities for chemical substitution and compositional modification. The thermodynamic favorability of the hydriding reaction is closely correlated with saturation capacity in materials-based hydrogen storage: the stronger a material absorbs hydrogen, the higher its capacity. This correlation is detrimental to most practical applications which require high gravimetric and/or volumetric capacity (e.g., vehicular and stationary storage) but relatively low ∆H (on the order of 27 kJ/molH 2 ) to achieve hydrogen release without excessive heating requirements. This fundamental trade-off is clearly captured in the HEA hydride predictions shown in Figure . Here we plot the absolute deviation of ∆H from a desired target enthalpy, in this case |∆H -27| kJ/molH 2 , versus the predicted saturation capacity in terms of H/M. In the presence of these competing objectives, the Pareto front is the set of materials for which both objectives cannot be simultaneously improved by any other material in the dataset. Figure shows a 4 dimensional Pareto front based on additional objectives, such as minimizing estimated raw material cost (USD/kg) and maximizing gravimetric saturation capacity, Hwt.% (as derived from predicted H/M and the molar mass of the composition). Finally, Figure shows the iterative evolution of the Pareto front as previous Pareto front materials are removed from the screening dataset.