To address some of these opportunities and further drive discovery with data-driven methods, we first improve upon our previously de-veloped hydride thermodynamic models. v0.0.4 of the ML-HydPARK training database was augmented to contain additional HEA hydride thermodynamic properties from literature studies when the necessary pressure-compositiontemperature (PCT) measurements were performed to extract the enthalpy and entropy of hydrogen desorption (∆H and ∆S) and saturation capacity (H/M). Thermodynamic properties from metal hydrides investigated for hydrogen compression were also added to the training data. We then expanded the scope of these models over our previous work by predicting new properties like H/M. Explainability analyses of these models importantly reveals the interdependence of different thermodynamic properties on the same material features, which leads to unavoidable and well-known trade-offs in hydride design, i.e. the enthalpy-capacity trade-off or the enthalpyentropy compensation effect. Both of these are detrimental for most proposed use cases of hydrides in stationary applications and are reflected when high-throughput screening a space of 10,000s of compositions with the ML models. This culminates in the elucidation of a Pareto optimal front of just 10s of materials (i.e., those whose individual properties can no longer simultaneously be improved) that should be targeted for experimental synthesis and testing.