融合语言与图结构，智能推荐新型聚合物。
PolyRecommender: A Multimodal Recommendation System for Polymer Discovery
- 用语言模型和分子图网络双模态建模聚合物。
- 先检索后排序，多属性精准匹配目标性能。
- 适合材料设计与高通量筛选场景。
我们提出PolyRecommender，一个融合化学语言表示（来自PolyBERT）与分子图表示（来自图编码器）的多模态发现框架。系统首先基于语言相似性检索候选聚合物，再通过融合的多模态嵌入对它们按多个目标属性进行排序。利用两种模态互补的知识，PolyRecommender在相关聚合物属性上实现了高效检索与鲁棒排序。本工作建立了一个可推广的多模态范式，推动下一代聚合物的AI辅助设计。
原文摘要 · Abstract (English)
We introduce PolyRecommender, a multimodal discovery framework that integrates chemical language representations from PolyBERT with molecular graph-based representations from a graph encoder. The system first retrieves candidate polymers using language-based similarity and then ranks them using fused multimodal embeddings according to multiple target properties. By leveraging the complementary knowledge encoded in both modalities, PolyRecommender enables efficient retrieval and robust ranking across related polymer properties. Our work establishes a generalizable multimodal paradigm, advancing AI-guided design for the discovery of next-generation polymers.
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