Bridging Small Molecule Calculations and Predictable Polymer Mechanical Properties

Research Highlight

• From Molecule to Material: We developed a predictive strategy that uses hydrogen-bond binding energy of small molecules to guide the synthesis of high-performance elastomers.
• One Calculation, Multiple Gains: Binding energy calculations not only capture the strength of supramolecular interactions but also directly inform molecular design, enabling faster, greener, and more cost-effective material development.
• Performance Breakthrough: The optimized SPU-HA combines exceptional toughness (1.1 GJ m⁻³), outstanding fatigue resistance, and UV durability, while maintaining scalability. SPU-HA also presents a performance-to-cost ratio double that of commercially available high-performance elastomers.
• Impact: This work establishes a theory–computation–experiment closed-loop framework for rational polymer design, paving the way for applications in flexible electronics, impact-resistant materials, and sustainable packaging.

Behind the Story

Polymeric materials, with their complex structures and diverse properties, are widely used in engineering, electronics, medicine, and many other fields. However, predicting material performance prior to synthesis and achieving rational design has long remained a major challenge in this field. Traditionally, researchers rely on a “trial-and-error” approach—synthesizing multiple polymer candidates, testing them, and iterating based on experimental results. This process is time-consuming, resource-intensive, and often environmentally costly.

Our idea was simple yet unconventional: What if we could predict polymer performance just by looking at small molecules?

By designing supramolecular chain extenders with different end groups (hydroxyl, amino, thiol, and hydrazide), while maintaining a consistent polymer backbone, we introduced two types of supramolecular interactions—“mismatched” and “matched”—to construct a series of polyurethane elastomers with controllable structures and comparable properties (Fig. 1). Combining density functional theory calculations with experimental results such as hydrogen bond content and physical crosslink density, we discovered that the binding energy between small molecular fragments correlates strongly with key polymer properties including tensile strength, elongation at break, and toughness, with correlation coefficients exceeding 0.88. This validates the strong predictive capability of small molecule calculations for macroscopic material performance. This computational guidance allowed us to bypass numerous failed experimental trials, directly targeting the most promising molecular architecture.

Fig. 1. Schematic diagram of the strategy for predicting the properties of supramolecular polymers based on small molecule calculations.

From Prediction to Reality

The representative material SPU-HA demonstrates outstanding comprehensive performance. In situ WAXS/SAXS experiments revealed reversible microstructural transitions under extreme strains, demonstrating remarkable resilience. Mechanical testing confirmed:

• Ultra-high toughness. The toughness is as high as 1.1 GJ m⁻³. Not only does it far surpass the currently reported colorless and transparent high-performance elastomers, but it also exceeds natural materials such as Darwin’s spider silk.
• Excellent fatigue resistance. Stable performance after 200% tension for 1000 loading cycles.
• UV durability. No significant degradation even after accelerated aging equivalent to over one year in tropical sunlight.
• Self-healing capability and recyclability.

Equally important, SPU-HA can be scaled up in a 20 L reactor without performance loss. Its estimated retail cost is $6.1/kg, offering a unique combination of high performance and economic viability.

Why It Matters

Our work represents more than just a new material—it introduces a predictive design philosophy for polymers:

1. Start with computation – use small-molecule models to understand supramolecular interactions.
2. Establish correlations – link binding energy with targeted mechanical properties.
3. Guide synthesis – prioritize candidates with optimal predicted interactions.
4. Validate and scale – confirm predictions experimentally and explore industrial feasibility.

This “computation‑to‑material” strategy shortens development cycles while reducing costs. It also minimizes waste, offering critical advantages for both academic research and industrial applications.

Looking Ahead

This study offers a simple and efficient route for performance prediction, providing both theoretical insight and practical tools for developing high‑performance elastomers. It shows that, with the polymer backbone fixed, tuning end‑group‑driven supramolecular interactions can markedly adjust strength and toughness, laying a solid foundation for a “computation‑guided synthesis” paradigm and guiding the design of future multi‑component, hierarchical supramolecular networks.

For more details, read our article Bridging Small Molecule Calculations and Predictable Polymer Mechanical Properties in Nature Communications.