Accelerating defect predictions in semiconductors using graph neural networks
First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordina...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2024-03-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0176333 |