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Your search keyword '"Navarro-Moratalla, Efrén"' showing total 7 results
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7 results on '"Navarro-Moratalla, Efrén"'

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

Author

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Research Laboratory of Electronics, 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, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Research Laboratory of Electronics, 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, and Palacios, Tomás

Abstract

© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 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.

Published
2021

3. Nanomagnets: Quantum Rescaling, Domain Metastability, and Hybrid Domain‐Walls in 2D CrI 3 Magnets (Adv. Mater. 5/2021)

Author

Wahab, Dina Abdul, primary, Augustin, Mathias, additional, Valero, Samuel Manas, additional, Kuang, Wenjun, additional, Jenkins, Sarah, additional, Coronado, Eugenio, additional, Grigorieva, Irina V., additional, Vera‐Marun, Ivan J., additional, Navarro‐Moratalla, Efrén, additional, Evans, Richard F. L., additional, Novoselov, Kostya S., additional, and Santos, Elton J. G., additional

Published
2021
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4. Quantum Rescaling, Domain Metastability, and Hybrid Domain‐Walls in 2D CrI3 Magnets

Author

Wahab, Dina Abdul, primary, Augustin, Mathias, additional, Valero, Samuel Manas, additional, Kuang, Wenjun, additional, Jenkins, Sarah, additional, Coronado, Eugenio, additional, Grigorieva, Irina V., additional, Vera‐Marun, Ivan J., additional, Navarro‐Moratalla, Efrén, additional, Evans, Richard F. L., additional, Novoselov, Kostya S., additional, and Santos, Elton J. G., additional

Published
2020
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5. Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials

Author

Han, Bingnan, primary, Lin, Yuxuan, additional, Yang, Yafang, additional, Mao, Nannan, additional, Li, Wenyue, additional, Wang, Haozhe, additional, Yasuda, Kenji, additional, Wang, Xirui, additional, Fatemi, Valla, additional, Zhou, Lin, additional, Wang, Joel I.‐Jan, additional, Ma, Qiong, additional, Cao, Yuan, additional, Rodan‐Legrain, Daniel, additional, Bie, Ya‐Qing, additional, Navarro‐Moratalla, Efrén, additional, Klein, Dahlia, additional, MacNeill, David, additional, Wu, Sanfeng, additional, Kitadai, Hikari, additional, Ling, Xi, additional, Jarillo‐Herrero, Pablo, additional, Kong, Jing, additional, Yin, Jihao, additional, and Palacios, Tomás, additional

Published
2020
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