论文标题
一种深入学习模型，用于快速预测各种材料中空置形成的模型
A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials
论文作者
论文摘要
空缺等点缺陷的存在在材料设计中起着重要作用。在这里，我们证明了仅在完美材料上训练的图形神经网络（GNN）模型也可以用于预测缺陷结构的空置形成能（$ e_ {vac} $），而无需其他培训数据。这种基于GNN的预测的速度要比密度功能理论（DFT）的速度快得多，并具有合理的精度，并表明了GNN能够捕获能量预测的功能形式的潜力。为了测试此策略，我们开发了由3D元素固体，合金，氧化物，氮化物和2D单层材料组成的508 $ e_ {vac} $的DFT数据集。我们分析并讨论了这种直接和快速预测的适用性。我们将模型应用于Jarvis-DFT数据库中的55723材料预测192494 $ e_ {vac} $。
The presence of point defects such as vacancies plays an important role in material design. Here, we demonstrate that a graph neural network (GNN) model trained only on perfect materials can also be used to predict vacancy formation energies ($E_{vac}$) of defect structures without the need for additional training data. Such GNN-based predictions are considerably faster than density functional theory (DFT) calculations with reasonable accuracy and show the potential that GNNs are able to capture a functional form for energy predictions. To test this strategy, we developed a DFT dataset of 508 $E_{vac}$ consisting of 3D elemental solids, alloys, oxides, nitrides, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192494 $E_{vac}$ for 55723 materials in the JARVIS-DFT database.
