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...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 multimodal and domain-specific information to enhance polymer property prediction,thereby advancing high-throughput screening and the discovery of novel polymer materials.展开更多
基金funded by the National Natural Science Foundation of China,No.62474183,The funder played no role in study design,data collection,analysis and interpretation of data,or the writing of this manuscript.
文摘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 multimodal and domain-specific information to enhance polymer property prediction,thereby advancing high-throughput screening and the discovery of novel polymer materials.
