Deep-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials.
2D materials
deep learning
machine learning
material characterization
optical microscopy
Journal
Advanced materials (Deerfield Beach, Fla.)
ISSN: 1521-4095
Titre abrégé: Adv Mater
Pays: Germany
ID NLM: 9885358
Informations de publication
Date de publication:
Jul 2020
Jul 2020
Historique:
received:
10
02
2020
revised:
13
05
2020
pubmed:
11
6
2020
medline:
11
6
2020
entrez:
11
6
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32519397
doi: 10.1002/adma.202000953
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2000953Subventions
Organisme : Army Research Office
ID : W911NF-18-2-0048
Organisme : AFOSR
ID : FA9550-15-1-0514
Organisme : National Natural Science Foundation of China
ID : 41871240
Organisme : National Science Foundation
ID : EFRI-1542815
Organisme : NSF
ID : DMR-1507806
Organisme : STC Center for Integrated Quantum Materials
Organisme : NSF
ID : DMR-599 1231319
Organisme : Office of Science
Organisme : DOE
Organisme : BES
ID : DE-SC0019300
Organisme : Gordon and Betty Moore Foundation
ID : GBMF4541
Organisme : China Scholarship Council
Organisme : NSF
ID : 1945364
Informations de copyright
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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