A Functional-Group Atlas for Decoupling Interfacial Behaviors in Thermal Energy Storage
Yifei Zhu1, Tiansheng Wang1, Yanfei Zhu1
1Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, P. R. China.
Advanced Materials (Deerfield Beach, Fla.)
|July 24, 2026
Summary
Researchers developed a machine learning approach to design advanced composite phase-change materials for thermal energy storage. This method optimizes material interfaces, significantly boosting energy and power densities while improving long-term stability.
Area of Science:
- Materials Science
- Energy Storage
- Computational Chemistry
Background:
- Composite phase-change materials (PCMs) are crucial for thermal energy storage (TES).
- A key challenge is the trade-off between energy and power densities due to interface limitations.
- Current interface engineering relies on empirical methods, failing to decouple wettability and heat transport.
Purpose of the Study:
- To introduce a machine learning-assisted design paradigm for optimizing PCM interfaces.
- To achieve programmable control over skeleton-molten salt interactions.
- To overcome the inherent energy-power density trade-off in TES systems.
Main Methods:
- Functional group deconstruction using machine learning to analyze surface properties.
- Resolving interfacial behaviors into elemental composition (wettability) and geometric topology (heat transport).
- Synthesis and characterization of targeted carbon-molten salt composites.
Main Results:
- Demonstrated intrinsic property decoupling: wettability linked to bonding, heat transport to phonon matching.
- Engineered composites showed 1.6x higher mass loading and 3.6x enhanced thermal conductivity.
- Achieved superior stability with ~90% mass and ~80% conductivity retention after 350 cycles.
Conclusions:
- The machine learning approach enables precise control over interfacial properties for TES materials.
- Optimized composites overcome the energy-power trade-off, tripling energy density under high charge/discharge rates.
- This strategy offers a scalable route for high-performance, durable thermal energy storage solutions.
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