Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks
Abstract Structural defects are abundant in solids, and vital to the macroscopic materials properties. However, a defect-property linkage typically requires significant efforts from experiments or simulations, and often contains limited information due to the breadth of nanoscopic design space. Here...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-09-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-022-00879-4 |