文献类型：期刊文献

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

作者：Lu, Yunhua[1,2]; Chen, Mingyue[1]; Zhang, Qingwei[1]; Zhang, Junan[1]; Zhang, Chao[3]; Xu, Shiai[3,4]; Bi, Qiuyan[3]

机构：[1] School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China; [2] Salt Lake Chemical Engineering Research Complex, Qinghai University, Xining, China; [3] School of Chemical Engineering, Qinghai University, Xining, China; [4] School of Materials Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2026

卷号：12

期号：1

外文期刊名：npj Computational Materials

收录：EI(收录号：20260519982069)

语种：英文

外文关键词：Benchmarking - Budget control - Convolution - Design for testability - Forecasting - Functional materials - Graphene - Learning systems - Materials properties - Multi-task learning - Neural networks - Personnel training - Quantum chemistry - Screening - Work function

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. ? The Author(s) 2025.