Stoichiometry-based machine learning enables discovery of new salt hydration reactions for thermochemical heat storage (opens in new tab)

Reversible hydration reactions of solid salts are most promising for sustainable heat storage, offering high energy densities, long-term cyclability, and tunable operational temperatures. Yet, the discovery of new salt hydrates with optimal thermochemical performance remains daunting. The chemical design space is enormous—exceeding 108 possible reactions, per our estimation—and experimental or simulation data is scarce—available for < 6000 reactions. Here, we introduce a robust machine-learni...