HiPoly框架能从实验数据直接预测聚合物性质并设计可持续替代品，比传统方法更高效准确。
HiPoly是一种基于G2RINS表示的三层分层图架构AI框架，可直接编码随机单体连接性、组成和分子量。该框架实现了从实验配方数据到物性预测、生成分子设计和基于物理验证的端到端工作流。研究团队展示了多组分聚合物系统热物理性能的最先进预测精度，消融研究证实每个分层设计选择独立贡献于模型性能。
HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design
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.