Machine Learning-Based Detection of Graphene Defects with Atomic PrecisionOACSCD
Machine Learning?Based Detection of Graphene Defects with Atomic Precision
Defects in graphene can pro-foundly 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 approach is to observe the thermal vibration properties of the graphene sheet, which reflects defect information but in an implicit fashion. Machine learning, an emerging d…查看全部>>
Bowen Zheng;Grace X. Gu
Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USADepartment of Mechanical Engineering, University of California, Berkeley, CA 94720, USA
Machine learningGrapheneDefectsMolecular dynamicsNanomaterials
Machine learningGrapheneDefectsMolecular dynamicsNanomaterials
《纳微快报（英文）》 2020 (12)
331-343,13
This work used the Extreme Science and Engineering Discovery Environment(XSEDE)Bridges system,which is supported by National Science Foundation Grant Num-ber ACI-1548562.The authors also acknowledge support from an NVIDIA GPU Seed Grant.
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