Abstract
The presence of point defects, such as vacancies, plays an important role in materials design. Here, we explore the extrapolative power of a graph neural network (GNN) to predict vacancy formation energies. We show that a model trained only on perfect materials can also be used to predict vacancy formation energies (Evac) of defect structures without the need for additional training data. Such GNN-based predictions are considerably faster than density functional theory (DFT) calculations and show potential as a quick pre-screening tool for defect systems. To test this strategy, we developed a DFT dataset of 530 Evac consisting of 3D elemental solids, alloys, oxides, semiconductors, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192 494 Evac for 55 723 materials in the JARVIS-DFT database. Our work demonstrates how a GNN-model performs on unseen data.
| Original language | English |
|---|---|
| Article number | 095109 |
| Journal | AIP Advances |
| Volume | 13 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 1 2023 |
Funding
K.C. thanks the National Institute of Standards and Technology for funding, computational, and data-management resources. DFT calculations were also conducted at the Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, a U.S. Department of Energy Office of Science User Facility. K.C. thanks the National Institute of Standards and Technology for funding, computational, and data-management resources. DFT calculations were also conducted at the Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, a U.S. Department of Energy Office of Science User Facility.