Machine Learning-Based Detection of Graphene Defects with Atomic Precision

被引：0
|
作者：
Bowen Zheng
Grace X. Gu
机构：
[1] University of California,Department of Mechanical Engineering
来源：
Nano-Micro Letters | 2020年 / 12卷
关键词：
Machine learning; Graphene; Defects; Molecular dynamics; Nanomaterials;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A machine learning-based approach is developed to predict the unknown defect locations by thermal vibration topographies of graphene sheets.Two prediction strategies are developed: an atom-based method which constructs data by atom indices, and a domain-based method which constructs data by domain discretization.Our machine learning model can achieve approximately a 90% prediction accuracy on the reserved data for testing, indicating a promising extrapolation into unseen future graphene configurations.
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