Machine Learning-Based Detection of Graphene Defects with Atomic Precision

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...

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Bibliographic Details
Main Authors: Bowen Zheng, Grace X. Gu
Format: Article
Language:English
Published: SpringerOpen 2020-09-01
Series:Nano-Micro Letters
Subjects:
Machine learning
Graphene
Defects
Molecular dynamics
Nanomaterials
Online Access:http://link.springer.com/article/10.1007/s40820-020-00519-w

Internet

http://link.springer.com/article/10.1007/s40820-020-00519-w

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