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

Abstract

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.