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 perfo...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.展开更多
基金supported by the key project of science and technology research program of Chongqing Education Commission of China(KJZD-K202501109)the National Natural Science Foundation of China(U22A20434)Scientific research foundation of Ministry of Industry and Information Technology of the People's Republic of China(TC220A04A-43).
文摘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.
