融合结构与分子特征，提升聚合物性能预测精度
Multi-modal cascade feature transfer for polymer property prediction
- 分层传递多模态特征，整合化学结构与分子描述符
- 在多个聚合物数据集上优于单一特征基线模型
- 适合材料设计与高通量筛选场景
本文提出一种新型迁移学习方法——多模态级联特征传递模型，用于聚合物性能预测。聚合物具有多种数据形式，包括分子描述符、添加剂信息和化学结构。传统方法通常单独使用每类数据构建模型。本模型通过图卷积神经网络（GCN）提取化学结构特征，并融合分子描述符与添加剂信息，实现更准确的物理性能预测。在多个聚合物数据集上的实证评估表明，该方法显著优于仅使用单一特征的基线模型。
原文摘要 · Abstract (English)
In this paper, we propose a novel transfer learning approach called multi-modal cascade model with feature transfer for polymer property prediction.Polymers are characterized by a composite of data in several different formats, including molecular descriptors and additive information as well as chemical structures. However, in conventional approaches, prediction models were often constructed using each type of data separately. Our model enables more accurate prediction of physical properties for polymers by combining features extracted from the chemical structure by graph convolutional neural networks (GCN) with features such as molecular descriptors and additive information. The predictive performance of the proposed method is empirically evaluated using several polymer datasets. We report that the proposed method shows high predictive performance compared to the baseline conventional approach using a single feature.
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