Deep Learning-Based Layer Identification of 2D Nanomaterials

Zhang, Heng; Zhou, Shujuan; Liu, Guangjie; Zhu, Jinlong

doi:10.3390/coatings12101551

Cited by 3 publications

(1 citation statement)

References 75 publications

(173 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on some open source data and public data sets of optical images of 2D materials, researchers in the computer field can more easily access and develop a variety of algorithms, where the accessibility of this intersecting area of different research fields will greatly facilitate the finding of more general and effective methods for layer identification of 2D materials. [261,262]…”

Section: Optical Image-based Determination Methodsmentioning

confidence: 99%

Thickness Determination of Ultrathin 2D Materials Empowered by Machine Learning Algorithms

Mao

Wang

Chen

et al. 2023

Laser & Photonics Reviews

View full text Add to dashboard Cite

The number of layers of 2D materials is of great significance for regulating the performance of nanoelectronic devices and optoelectronic devices, where the optimal thickness of the target sample should be determined before further physical research or device manufacturing steps. At present, a variety of different optical technologies have been proposed to determine the thickness of samples by using the relationship between the number of layers and optical properties, including optical contrast, optical imaging, Raman spectra, photoluminescence spectra, nonlinear spectra, near‐field optical imaging, ellipsometry spectra, and hyperspectral imaging. In the past decade, the rapidly growing number of 2D materials and their heterostructures has exceeded the capacity of traditional experimental and computational methods. In recent years, machine learning (ML) is emerging as a powerful tool to support such traditional methods, which brings new opportunities to tap the potential of optical technology in a more perspicacious way. The application of optical technology has greatly facilitated the acquisition of optical information of materials, while ML algorithms provide a fast, high‐throughput, and intelligent way to complete back‐end data processing and inference. A profound integration of conventional optical technologies and ML algorithms significantly helps 2D materials progress in basic studies and practical applications and further promotes industrial manufacturing.

show abstract

Section: Optical Image-based Determination Methodsmentioning

confidence: 99%

Thickness Determination of Ultrathin 2D Materials Empowered by Machine Learning Algorithms

Mao

Wang

Chen

et al. 2023

Laser & Photonics Reviews

View full text Add to dashboard Cite

show abstract

Deep learning in two-dimensional materials: Characterization, prediction, and design

Meng,

Qin,

Liang

et al. 2024

Front. Phys.

View full text Add to dashboard Cite

Since the isolation of graphene, two-dimensional (2D) materials have attracted increasing interest because of their excellent chemical and physical properties, as well as promising applications. Nonetheless, particular challenges persist in their further development, particularly in the effective identification of diverse 2D materials, the domains of large-scale and high-precision characterization, also intelligent function prediction and design. These issues are mainly solved by computational techniques, such as density function theory and molecular dynamic simulation, which require powerful computational resources and high time consumption. The booming deep learning methods in recent years offer innovative insights and tools to address these challenges. This review comprehensively outlines the current progress of deep learning within the realm of 2D materials. Firstly, we will briefly introduce the basic concepts of deep learning and commonly used architectures, including convolutional neural and generative adversarial networks, as well as U-net models. Then, the characterization of 2D materials by deep learning methods will be discussed, including defects and materials identification, as well as automatic thickness characterization. Thirdly, the research progress for predicting the unique properties of 2D materials, involving electronic, mechanical, and thermodynamic features, will be evaluated succinctly. Lately, the current works on the inverse design of functional 2D materials will be presented. At last, we will look forward to the application prospects and opportunities of deep learning in other aspects of 2D materials. This review may offer some guidance to boost the understanding and employing novel 2D materials.

show abstract

Two-Dimensional Materials-Based Thin Films and Coatings

Tene,

Tubon-Usca,

Haro-Barroso

et al. 2024

Coatings

View full text Add to dashboard Cite

Here, we highlight the research presented in this Special Issue, focusing on the innovative use of graphene and other two-dimensional (2D) materials to develop advanced coating technologies. The contributions herein address critical challenges such as the scalable fabrication and stable dispersion of such materials and their compatibility with conventional coating systems, offering solutions that enhance their mechanical strength, chemical stability, and multifunctionality. The featured studies demonstrate the diverse applications of these materials, from protective anticorrosive barriers to high-performance optoelectronic devices and environmental remediation. Moving forward, future research is encouraged to explore novel 2D materials, hybrid coating strategies, and advanced computational modeling to overcome existing limitations and unlock new possibilities.

show abstract

Deep Learning-Based Layer Identification of 2D Nanomaterials

Cited by 3 publications

References 75 publications

Thickness Determination of Ultrathin 2D Materials Empowered by Machine Learning Algorithms

Thickness Determination of Ultrathin 2D Materials Empowered by Machine Learning Algorithms

Deep learning in two-dimensional materials: Characterization, prediction, and design

Two-Dimensional Materials-Based Thin Films and Coatings

Contact Info

Product

Resources

About