当前位置: X-MOL 学术Extreme Mech. Lett. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Automated identification and characterization of two-dimensional materials via machine learning-based processing of optical microscope images
Extreme Mechanics Letters ( IF 4.7 ) Pub Date : 2020-05-18 , DOI: 10.1016/j.eml.2020.100771
Juntan Yang , Haimin Yao

Mechanical characterization of two-dimensional (2D) materials has always been a challenging task due to their extremely small thickness. The current prevailing methods to measure the strength of 2D materials normally involve sophisticated testing facilities and complicated procedures of sample preparation, which are usually costly and time-consuming. In this paper, we propose a cost-effective and rapid approach to characterizing the strength of 2D materials by processing optical microscope images of the mechanically exfoliated 2D materials. Specifically, a machine learning-based model is developed to automate the identification of 2D material flakes of different layers from the optical microscope images, followed by the determination of their lateral size. The statistical distribution of the flakes’ size is obtained and used to estimate the strength of the associated 2D material based on a distribution-property relationship we developed before. A case study with graphene indicates that the present machine learning-based method, as compared to the previous manual one, enhances the efficiency of characterization by more than one order of magnitude with no sacrifice of the accuracy.



中文翻译:

通过基于机器学习的光学显微镜图像处理,自动识别和表征二维材料

由于二维(2D)材料的厚度极小,因此对其进行机械表征一直是一项艰巨的任务。当前用于测量2D材料强度的流行方法通常涉及复杂的测试设备和复杂的样品制备程序,这通常既昂贵又耗时。在本文中,我们提出了一种经济有效的快速方法,通过处理机械剥落的2D材料的光学显微镜图像来表征2D材料的强度。具体而言,开发了一种基于机器学习的模型,以从光学显微镜图像中自动识别不同层的2D材料薄片,然后确定其横向尺寸。获得了薄片尺寸的统计分布,并根据我们之前开发的分布-特性关系,将其用于估算关联2D材料的强度。以石墨烯为例的研究表明,与先前的手册相比,本基于机器学习的方法将特征化效率提高了一个数量级以上,而丝毫没有牺牲准确性。

更新日期:2020-05-18
down
wechat
bug