4.8 Article

Deep-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials

期刊

ADVANCED MATERIALS
卷 32, 期 29, 页码 -

出版社

WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202000953

关键词

2D materials; deep learning; machine learning; material characterization; optical microscopy

资金

  1. U.S. Army Research Office through the Institute for Soldier Nanotechnologies [W911NF-18-2-0048]
  2. AFOSR FATE MURI [FA9550-15-1-0514]
  3. National Natural Science Foundation of China [41871240]
  4. National Science Foundation Grant 2DARE [EFRI-1542815]
  5. NSF [DMR-1507806, 1945364]
  6. STC Center for Integrated Quantum Materials, NSF [DMR-599 1231319]
  7. DOE Office of Science, BES [DE-SC0019300]
  8. Gordon and Betty Moore Foundation [GBMF4541]
  9. China Scholarship Council
  10. Direct For Mathematical & Physical Scien
  11. Division Of Chemistry [1945364] Funding Source: National Science Foundation

向作者/读者索取更多资源

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.

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