A Functional-Group Atlas for Decoupling Interfacial Behaviors in Thermal Energy Storage.

Composite phase-change materials offer a scalable route for thermal energy storage, yet breaking the inherent trade-off between energy and power densities is constrained by fundamental mismatches at the skeleton-storage-medium interface. Conventional interface engineering remains trapped in empirical trial-and-error, struggling to distinguish the typically entangled variables of interfacial wettability and heat-transport behavior. Here, we present a machine learning-assisted design paradigm based on functional group deconstruction. By resolving surface functional groups into independent elemental and structural dimensions, we achieve programmable control over skeleton-molten salt interfacial behaviors. We reveal an intrinsic property decoupling: interfacial wettability is governed by bonding interactions derived from elemental composition, whereas heat transport is dictated by low-frequency phonon spectral matching rooted in geometric topology. Guided by this predictive atlas, we synthesized targeted carbon-molten salt composites. Compared to unmodified baselines, the engineered composite achieves 1.6-fold higher mass loading and 3.6-fold enhanced thermal conductivity. Crucially, after 350 thermal cycles, it retains ∼90% mass and ∼80% conductivity, decisively suppressing the degradation of pristine hosts (∼60% and ∼25% retention). Device-level finite-difference method simulations indicate this dual-property optimization effectively overcomes the inherent energy-power trade-off-sustaining triple the energy density of unmodified baselines under extreme 10C constant-power loads.