Han Bingnan, Lin Yuxuan, Yang Yafang, Mao Nannan, Li Wenyue, Wang Haozhe, Yasuda Kenji, Wang Xirui, Fatemi Valla, Zhou Lin, Wang Joel I-Jan, Ma Qiong, Cao Yuan, Rodan-Legrain Daniel, Bie Ya-Qing, Navarro-Moratalla Efrén, Klein Dahlia, MacNeill David, Wu Sanfeng, Kitadai Hikari, Ling Xi, Jarillo-Herrero Pablo, Kong Jing, Yin Jihao, Palacios Tomás
Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China.
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Adv Mater. 2020 Jul;32(29):e2000953. doi: 10.1002/adma.202000953. Epub 2020 Jun 9.
Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, the optical characterization of 2D materials is used as a case study, and a neural-network-based algorithm is demonstrated for the material and thickness identification of 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, flake sizes, and their distributions, based on which an ensemble approach is developed to predict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other optical identification applications. This artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials, and potentially accelerate new material discoveries.
先进的显微镜和/或光谱工具在纳米科学和纳米技术研究中发挥着不可或缺的作用，因为它们能提供有关材料过程和特性的丰富信息。然而，成像数据的解读在很大程度上依赖于经验丰富的研究人员的“直觉”。因此，通过这些工具获得的许多深层图形特征往往未被利用，原因在于数据处理和寻找相关性存在困难。深度学习可以很好地应对此类挑战。在这项工作中，以二维材料的光学表征为例进行研究，并展示了一种基于神经网络的算法，该算法能够以高预测精度和实时处理能力对二维材料的材料类型和厚度进行识别。进一步分析表明，经过训练的网络可以提取对比度、颜色、边缘、形状、薄片尺寸及其分布等深层图形特征，并在此基础上开发了一种集成方法来预测二维材料最相关的物理特性。最后，应用迁移学习技术使预训练网络适用于其他光学识别应用。这种基于人工智能的材料表征方法是一种强大的工具，它将加快二维材料和其他纳米材料的制备、初始表征，并有可能加速新材料的发现。
