Problem
Research questions and friction points this paper is trying to address.
polymeric materials
hierarchical structure
representation
artificial intelligence
materials discovery
HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design
🤖 AI Summary
HiPoly通过三层分级图架构处理聚合物描述，解决了聚合物难以统一物理表示的问题，实现了从实验数据到性质预测和分子设计的全流程AI驱动。
📝 Abstract
Polymeric materials are central to modern technologies, with applications ranging from energy to health and transportation. Although AI has made significant advances in materials discovery, the hierarchical structure of polymers across multiple length scales makes them inherently difficult to represent in a unified and physically meaningful way. Here we introduce HiPoly, a polymer-native AI framework that processes complete polymer descriptions through a three-level hierarchical graph architecture built on the G2RINS representation. HiPoly encodes stochastic inter-monomer connectivity, composition, and molecular weight directly within its architecture, using physically motivated design principles that mirror the multi-scale nature of polymeric systems. The framework establishes an end-to-end AI-driven workflow from experimental formulation data to property prediction, generative molecular design, and physics-based validation through molecular simulations, all unified by a single polymer representation. We demonstrate state-of-the-art prediction accuracy for thermophysical properties of multi-component polymer systems, with ablation studies confirming that each hierarchical design choice contributes independently to model performance. As an example, the generative design pathway is applied here to the discovery of sustainable alternatives to persistent fluorinated polymers, where it is possible to identify and independently validate PFAS-free candidates with target surface-energy properties. This work demonstrates how polymer-native AI can accelerate discovery by linking representation, prediction, and design across complex polymer chemistries.
Innovation
Methods, ideas, or system contributions that make the work stand out.
hierarchical graph architecture
G2RINS representation
polymer-native AI
end-to-end workflow
generative design
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