Machine Learning-Based Detection of Graphene Defects with Atomic Precision
Abstract Defects in graphene can profoundly impact its extraordinary properties, ultimately influencing the performances of graphene-based nanodevices. Methods to detect defects with atomic resolution in graphene can be technically demanding and involve complex sample preparations. An alternative ap...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-09-01
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| Series: | Nano-Micro Letters |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s40820-020-00519-w |