Machine learning potential assisted exploration of complex defect potential energy surfaces
Abstract Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties. Unraveling the energetically most favorable ground-state configurations of these defects is an important step towards the fundamen...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
|
| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01207-8 |