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<p>The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties. For boron-doped graphene (BDG), both the band gap and work function critically influence performance in electronic and catalytic applications, yet existing machine learning (ML) approaches typically focus on single-property prediction and rely on hand-crafted features, limiting their generality. Here we present an adaptive edge-aware graph convolutional neural network with multi-task learning (AEGCNN-MTL) for simultaneous prediction of multiple material properties. On a DFT-computed BDG dataset of 2613 structures, AEGCNN-MTL achieved high accuracy (R² = 0.9905 for band gap and 0.9778 for work function), and under identical training budgets, outperformed representative single-task GNN baselines. When transferred to the QM9 benchmark, the framework delivered competitive performance across 12 diverse quantum chemical properties, demonstrating strong generalization capability. These results highlight the potential of AEGCNN-MTL as a scalable and accurate tool for high-throughput, multi-property screening and the data-driven discovery of multifunctional materials.</p>

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Adaptive edge-aware graph convolutional with multi-task learning for simultaneous prediction of material properties

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月24日

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  • Yunhua Lu,
  • Mingyue Chen,
  • Qingwei Zhang,
  • Junan Zhang,
  • Chao Zhang,
  • Shiai Xu,
  • Qiuyan Bi

摘要

The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties. For boron-doped graphene (BDG), both the band gap and work function critically influence performance in electronic and catalytic applications, yet existing machine learning (ML) approaches typically focus on single-property prediction and rely on hand-crafted features, limiting their generality. Here we present an adaptive edge-aware graph convolutional neural network with multi-task learning (AEGCNN-MTL) for simultaneous prediction of multiple material properties. On a DFT-computed BDG dataset of 2613 structures, AEGCNN-MTL achieved high accuracy (R² = 0.9905 for band gap and 0.9778 for work function), and under identical training budgets, outperformed representative single-task GNN baselines. When transferred to the QM9 benchmark, the framework delivered competitive performance across 12 diverse quantum chemical properties, demonstrating strong generalization capability. These results highlight the potential of AEGCNN-MTL as a scalable and accurate tool for high-throughput, multi-property screening and the data-driven discovery of multifunctional materials.