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HighlightsA 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.Electronic supplementary material  The online version of this article (10.1007/s40820-020-00519-w) contains supplementary material, which is available to authorized users.

Machine Learning-Based Detection of Graphene Defects with Atomic Precision