High-throughput screening of metal hydride thermodynamic properties is a significant challenge for several reasons. The complexity of hydrogen-metal interactions necessitates electronic structure methods to compute configurational energetics, and there are seldom sufficiently accurate and transferable force fields that are valid across the periodic table. Resorting to DFT for energy calculations is therefore necessary but vastly too expensive to evaluate the millions of configurations needed by Monte Carlo to simulate hydrogen uptake at a single chemical potential and temperature state point in a single material. Hydrides typically undergo a phase transition which only further complicates the classical simulation of open ensemble thermodynamics. However, compositional machine learning models, trained directly on experimentally measured data for the (de)hydriding reactions, provide a tractable path forward. While this approach is not without its own limitations (see previous work for detailed discussions on the methodology), this modeling strategy can suitably predict metal hydride thermodynamics and has been used to target and experimentally synthesize destabilized HEA hydrides. Gradient boosting tree regressor models are trained on the augmented v0.0.4 of ML-HydPARK with hyperparameters tuned to minimize over-fitting and K-fold cross validation test set errors. The first through fourth rows of Figure respectively summarize the models for hydrogen-to-metal saturation capacity, H/M, enthalpy of desorption, ∆H [kJ/molH 2 ], entropy of desorption, ∆S [J/(molH 2 •K)], and direct prediction of log equilibrium plateau pressure at 25 ) (which is related to direct prediction of ∆G). Figure shows density parity plots for the test set predictions from a random K=10-fold cross validation across the entire dataset. The expected mean absolute error (MAE) assessed across Kfold test sets, ⟨MAE⟩ K , for each model type is shown in Table . ) , arises from a linear correlation with νpa which be used as a simple first-order design rule to tune the stability of metal hydrides. Another important observation is that separate models have significant overlap in the 5 most important features, which lends a rough interpretation that it is difficult to independently tune the thermodynamic properties of metal hydrides. For example, note how ∆H's dependence on ∆ Hb and SG# are inversely correlated with ∆S's, indicative of the enthalpy/entropy compensation effect that generally constrains materials design. Figure breaks down the K-fold test set errors against the underlying data distribution. Higher accuracy is directly correlated with the density of training data for a given target property; thus, the highly non-uniform distribution of all measured properties in ML-HydPARK presents a challenge but also a significant opportunity for model improvement with the collection of additional data. The challenge is most pronounced when predicting materials at the extrema of the property ranges. For example, there are few hydrogen storage applications where one would not want to maximize H/M, but the lack of data for H/M≥2 makes predictions in this regime difficult. Another example is the low density of training data for ∆H < ∼20 kJ/molH 2 , which is the typical region in which promising materials for hydrogen compression are found. Nonetheless, the models are sufficiently accurate that we can highthroughput screen HEA compositions for primary down-selection of hydrides that should be prioritized for experimental testing. Furthermore, we anticipate model accuracy will continue to improve as more experimental data is collected.