Compositional machine learning models of metal hydride thermodynamics provide an excellent tool to highthroughput screen hydride thermodynamic properties for new alloy compositions, but their performance degrades on highly out-of-distribution materials' predictions. These include ultra-low stability hydrides (e.g., those useful for high pressure hydrogen compression), since the difficulty of PCT measurements in pressure regimes greater than 100-200 bar has led to a dearth of experimental characterization of these types of alloys. Therefore, we utilized SHAP explainability analyses of these models to obtain the composition-property design rules, and subsequent chemical substitution strategies, that are most likely to further destabilize an existing alloy's hydride phase. We used one such substitution strategy to further destabilize an already promising metal hydride for hydrogen compression, then fully synthesized, characterized, and tested TCMCFL, including high-pressure PCT measurements to confirm its ability to compress hydrogen up to 875 bar.