Unified Multimodal Multidomain Polymer Representation for Property Prediction 

Polymer property prediction is a critical task in polymer science. Conventional approaches typically rely on a single data modality or a limited set of modalities, which constrains both predictive accuracy and practical applicability. In this paper, we present Uni-Poly, a novel framework that integrates diverse data modalities to achieve a comprehensive and unified representation of polymers. Uni-Poly encompasses all commonly used structural formats, including SMILES, 2D graphs, 3D geometries, and fingerprints. In addition, it incorporates domain-specific textual descriptions to enrich the representation. Experimental results demonstrate that Uni-Poly outperforms all single-modality and multi-modality baselines across various property prediction tasks. The integration of textual descriptions provides complementary information that structural representations alone cannot capture. These findings underscore the value of leveraging multi-modal and domain-specific information to enhance polymer property prediction, thereby advancing high-throughput screening and the discovery of novel polymer materials.