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<p>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.</p>

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Unified multimodal multidomain polymer representation for property prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月26日

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  • Qi Huang,
  • Yedi Li,
  • Lei Zhu,
  • Qibin Zhao,
  • Wenjie Yu

摘要

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.