Optimal materials for hydrogen compression. The principle of hydrogen compression using a metal hydride is based on the thermodynamic relationship described by the van't Hoff equation, which correlates the equilibrium pressure of hydrogen with temperature, enthalpy, and entropy changes associated with hydride formation. Figure shows a schematic representation of hydrogen compression using a metal hydride, illustrating hydrogen absorption at a low temperature (T L ) and low pressure (P L ), followed by desorption at a higher temperature (T H ), generating an elevated pressure (P H ). At reduced temperature T L , the metal hydride absorbs hydrogen gas at low pressure P L to form a stable hydride phase, a process that is thermodynamically exothermic. Upon heating to temperature T H , the chemical equilibrium shifts, favoring the endothermic desorption of hydrogen, thereby elevating the hydrogen delivery pressure P H . The compression ratio is defined as the ratio of the high Figure : Schematic representation of hydrogen compression using a metal hydride. Hydrogen is absorbed at temperature T L and H 2 pressure P L , and desorbed upon heating at temperature T H , generating pressure P H . pressure at desorption (P H ) to the low pressure at absorption (P L ). This behavior underlies the functionality of metal hydride-based hydrogen compressors, where cyclic cooling and heating enable hydrogen absorption at low pressures and subsequent desorption at high pressures. Effective thermal management, achieved through the incorporation of high-conductivity materials such as copper, aluminum, or various carbons, is imperative to facilitate rapid heat transfer and uniform temperature distribution within the hydride bed. Explainable ML design rules for hydride destabilization. Compositional ML models have been widely used to predict metal hydride thermodynamic properties and subsequently screen for new or modified (high entropy) alloys with targeted thermodynamic destabilization. See Supplementary Information (SI) for more details on the ML methodology. Briefly, descriptors derived purely from a given alloy composition are generated using Magpie. These features correspond to, for example, the alloy composition-weighted averages over an elemental property, p = i x i p i (x i ≡ atomic fraction of element i in the alloy, p i ≡ the elemental property) and serve as input to a gradient boosting regressor 23 machine learning model trained to predict thermodynamic properties of Equation (1), i.e., ln(P o eq /P o ), ∆H d , and/or ∆S d ,