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arXiv 2205.08366cond-mat.mtrl-sci
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用于快速预测多种材料中空位形成的深度学习模型

A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials

Kamal Choudhary, Bobby G. Sumpter

AI总结:

本文提出一种仅在完美材料上训练的图神经网络模型,能无需额外数据快速预测多种缺陷结构的空位形成能,其速度远超DFT计算且精度合理,并成功应用于JARVIS-DFT数据库的大规模预测。

摘要:

空位等点缺陷的存在在材料设计中起着重要作用。在此,我们证明仅在完美材料上训练的图神经网络(GNN)模型也可用于预测缺陷结构的空位形成能($E_{vac}$),而无需额外的训练数据。这种基于GNN的预测比密度泛函理论(DFT)计算快得多,且具有合理的精度,并显示出GNN能够捕捉用于能量预测的泛函形式的潜力。为了测试这一策略,我们构建了一个包含508个$E_{vac}$的DFT数据集,涵盖3D元素固体、合金、氧化物、氮化物和2D单层材料。我们分析并讨论了这种直接且快速的预测的适用性。我们将该模型应用于JARVIS-DFT数据库中,预测了55723种材料的192494个$E_{vac}$。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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