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<p>Conductive polymer nanocomposites have emerged as essential materials for wearable devices. In this study, we propose a novel approach that combines graph attention networks (GAT) with an improved global pooling strategy and incremental learning. We train the GAT model on homopolymer/carbon nanotube (CNT) nanocomposite data simulated by hybrid particle-field molecular dynamics (hPF-MD) method within the CNT concentration range of 1–8%. We further analyze the conductive network structure by integrating the resistor network approach with the GAT’s attention scores, revealing optimal connectivity at a 7% concentration. The comparative analysis of trained data and the reconstructed network, based on the attention scores, underscores the GAT model’s ability in learning network structural representations. This work not only validates the efficacy of the GAT model in property prediction and interpretable network structure analysis of polymer nanocomposites but also lays a cornerstone for the reverse engineering of polymer composites.</p>

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Graph attention networks decode conductive network mechanism and accelerate design of polymer nanocomposites

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月28日

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  • Tang Sui,
  • Shaolong Liu,
  • Bihui Cong,
  • Xiaoke Xu,
  • Dongjing Shan,
  • Giuseppe Milano,
  • Ying Zhao,
  • Shuang Xu,
  • Jiashun Mao

摘要

Conductive polymer nanocomposites have emerged as essential materials for wearable devices. In this study, we propose a novel approach that combines graph attention networks (GAT) with an improved global pooling strategy and incremental learning. We train the GAT model on homopolymer/carbon nanotube (CNT) nanocomposite data simulated by hybrid particle-field molecular dynamics (hPF-MD) method within the CNT concentration range of 1–8%. We further analyze the conductive network structure by integrating the resistor network approach with the GAT’s attention scores, revealing optimal connectivity at a 7% concentration. The comparative analysis of trained data and the reconstructed network, based on the attention scores, underscores the GAT model’s ability in learning network structural representations. This work not only validates the efficacy of the GAT model in property prediction and interpretable network structure analysis of polymer nanocomposites but also lays a cornerstone for the reverse engineering of polymer composites.