发表机构
New York University; University of Chicago; Peking University（纽约大学; 芝加哥大学; 北京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
HiPoly是基于G2RINS的分层聚合物原生AI框架，可处理完整聚合物描述，实现从配方数据到性能预测、生成式设计及物理验证的端到端工作流，在多组分聚合物热性能预测中达最优精度，能加速可持续聚合物替代物发现。
AI 中文摘要
聚合物材料是现代技术的核心，应用范围涵盖能源、健康与交通领域。尽管人工智能在材料发现方面已取得显著进展，但聚合物跨多个长度尺度的分层结构使其难以用统一且具有物理意义的方式表征。本文提出HiPoly，一种基于G2RINS表征构建的三级分层图架构的聚合物原生AI框架，可处理完整的聚合物描述。HiPoly通过符合聚合物系统多尺度特性的物理驱动设计原则，直接在架构中编码随机单体间连接性、组成与分子量。该框架建立了从实验配方数据到性能预测、生成式分子设计，再到通过分子模拟进行基于物理的验证的端到端AI驱动工作流，所有环节均由单一聚合物表征统一。我们在多组分聚合物体系的热物理性能预测中展现了最先进的精度，消融研究证实每一项分层设计选择均独立提升模型性能。作为示例，生成式设计路径被用于发现持久性氟化聚合物的可持续替代方案，可识别并独立验证具备目标表面能性质的无PFAS候选物。本研究证明，聚合物原生AI可通过关联复杂聚合物化学的表征、预测与设计，加速材料发现进程。
英文摘要
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.
