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

Abstract

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.

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Cited by in corpus (7)

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• Machine-learning structural reconstructions for accelerated point defect calculations
• Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
• InterMat: Accelerating Band Offset Prediction in Semiconductor Interfaces with DFT and Deep Learning
• The JARVIS Infrastructure is All You Need for Materials Design
• DeFecT-FF: a machine learning force field framework for high throughput defect modeling in CdTe-based solar cells