标题： 基于图神经网络的深度学习预测结构和电子性质 显示英文标题

标题： Graph Neural Networks Based Deep Learning for Predicting Structural and Electronic Properties

摘要： 本研究提出了一种深度学习方法，使用图神经网络（GNNs）预测材料的结构和电子性质。 利用Materials Project数据库中的数据，我们构建了晶体结构的图表示，并使用GNNs同时预测多种性质。 所有晶体结构均来自Materials Project数据库，共使用了158,874个结构。 我们的模型在各种性质上的预测精度较高，如\( R^2 \)值所示：密度为0.96，形成能为0.97，超出壳层能量为0.54，结构稳定性（is_S）为0.47，带隙为0.76，价带最大值为0.86，导带最小值为0.78，费米能级为0.82。 这些结果展示了GNNs在材料科学中的潜力，为快速筛选和发现具有所需性质的材料提供了一个强大的工具。 显示英文摘要

摘要： This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials Project database, we construct graph representations of crystal structures and employ GNNs to predict multiple properties simultaneously. All crystal structures are from the Materials Project database, with a total of 158,874 structures used. Our model achieves high predictive accuracy across various properties, as indicated by \( R^2 \) values: 0.96 for density, 0.97 for formation energy, 0.54 for energy above hull, 0.47 for structural stability (is\_S), 0.76 for band gap, 0.86 for valence band maximum, 0.78 for conduction band minimum, and 0.82 for Fermi energy. These results demonstrate the potential of GNNs in materials science, offering a powerful tool for rapid screening and discovery of materials with desired properties.