• 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.

中文翻译：

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基于机器学习的原子精确度石墨烯缺陷检测

• 开发了一种基于机器学习的方法，以通过石墨烯片的热振动形貌预测未知缺陷的位置。

• 开发了两种预测策略：基于原子的方法通过原子索引构造数据，以及基于域的方法通过域离散化数据。

• 我们的机器学习模型可以在保留的数据上进行测试，从而达到约90％的预测准确度，这表明可以将其推广到看不见的未来石墨烯配置中。