Machine Learning-Based Detection of Graphene Defects with Atomic Precision

Zheng, Bowen; Gu, Grace X.

doi:10.1007/s40820-020-00519-w

Cited by 24 publications

(15 citation statements)

References 56 publications

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“…Future work may include discovering the compatibility of the geometric equation, in which different material and printing parameters are used in the fabrication process. Finding other fish scale parameters, such as the thickness of the fish scale and the width of the 3D model, as well as applying optimization and machine learning techniques, could further enhance the accuracy of the curvature prediction [ 42 , 43 , 44 , 45 , 46 ]. On the other hand, using the geometric equation as the basic principle, a prediction tool for flexible material designs can also be developed to accommodate a more complex 3D fish scale structure.…”

Section: Discussionmentioning

confidence: 99%

Modeling Bioinspired Fish Scale Designs via a Geometric and Numerical Approach

et al. 2021

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Fish scales serve as a natural dermal armor with remarkable flexibility and puncture resistance. Through studying fish scales, researchers can replicate these properties and tune them by adjusting their design parameters to create biomimetic scales. Overlapping scales, as seen in elasmoid scales, can lead to complex interactions between each scale. These interactions are able to maintain the stiffness of the fish’s structure with improved flexibility. Hence, it is important to understand these interactions in order to design biomimetic fish scales. Modeling the flexibility of fish scales, when subject to shear loading across a substrate, requires accounting for nonlinear relations. Current studies focus on characterizing these kinematic linear and nonlinear regions but fall short in modeling the kinematic phase shift. Here, we propose an approach that will predict when the linear-to-nonlinear transition will occur, allowing for more control of the overall behavior of the fish scale structure. Using a geometric analysis of the interacting scales, we can model the flexibility at the transition point where the scales start to engage in a nonlinear manner. The validity of these geometric predictions is investigated through finite element analysis. This investigation will allow for efficient optimization of scale-like designs and can be applied to various applications.

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Section: Discussionmentioning

confidence: 99%

Modeling Bioinspired Fish Scale Designs via a Geometric and Numerical Approach

et al. 2021

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“…Inverse problems arise in many scientific and engineering fields and are typically difficult to solve by conventional approaches [15][16][17][18] . Much progress towards artificial intelligence (AI) and machine learning (ML) have been made and provided novel directions to solve these inverse problems [19][20][21][22] . For instance, ML techniques were applied to solve inverse problems in materials design [23][24][25] , fluid mechanics 26 , and PDEs 27 .…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, much progress toward artificial intelligence and machine learning (ML) has been made and provided novel directions to solve inverse problems. For instance, ML techniques were applied to inverse problems in materials design (24)(25)(26), fluid mechanics (27,28), and many others (29)(30)(31)(32)(33)(34). To obtain useful information from an elasticity image (e.g., to identify potential tumors), the number of pixels (resolution) is typically on the order of 10 3 to 10 5 .…”

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confidence: 99%

Learning hidden elasticity with deep neural networks

Chen

2021

Proc. Natl. Acad. Sci. U.S.A.

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Elastography is an imaging technique to reconstruct elasticity distributions of heterogeneous objects. Since cancerous tissues are stiffer than healthy ones, for decades, elastography has been applied to medical imaging for noninvasive cancer diagnosis. Although the conventional strain-based elastography has been deployed on ultrasound diagnostic-imaging devices, the results are prone to inaccuracies. Model-based elastography, which reconstructs elasticity distributions by solving an inverse problem in elasticity, may provide more accurate results but is often unreliable in practice due to the ill-posed nature of the inverse problem. We introduce ElastNet, a de novo elastography method combining the theory of elasticity with a deep-learning approach. With prior knowledge from the laws of physics, ElastNet can escape the performance ceiling imposed by labeled data. ElastNet uses backpropagation to learn the hidden elasticity of objects, resulting in rapid and accurate predictions. We show that ElastNet is robust when dealing with noisy or missing measurements. Moreover, it can learn probable elasticity distributions for areas even without measurements and generate elasticity images of arbitrary resolution. When both strain and elasticity distributions are given, the hidden physics in elasticity—the conditions for equilibrium—can be learned by ElastNet.

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“…The development of machine learning approaches enables predictions of target properties using learned patterns from data, and has been gaining momentum in materials prediction, design, and discovery [19][20][21][22][23][24][25][26]. Recently, machine learning has been applied to predict defect distribution [27], mechanical responses [28], chemical compositions [29], viscosity [30], among other nanomaterial properties of importance. However, most machine learning techniques require the size of material systems used in training to be identical to the ones they predict.…”

Section: Introductionmentioning

confidence: 99%

Scalable Graphene Defect Prediction Using Transferable Learning

Zheng

2021

Nanomaterials

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Notably known for its extraordinary thermal and mechanical properties, graphene is a favorable building block in various cutting-edge technologies such as flexible electronics and supercapacitors. However, the almost inevitable existence of defects severely compromises the properties of graphene, and defect prediction is a difficult, yet important, task. Emerging machine learning approaches offer opportunities to predict target properties such as defect distribution by exploiting readily available data, without incurring much experimental cost. Most previous machine learning techniques require the size of training data and predicted material systems of interest to be identical. This limits their broader application, because in practice a newly encountered material system may have a different size compared with the previously observed ones. In this paper, we develop a transferable learning approach for graphene defect prediction, which can be used on graphene with various sizes or shapes not seen in the training data. The proposed approach employs logistic regression and utilizes data on local vibrational energy distributions of small graphene from molecular dynamics simulations, in the hopes that vibrational energy distributions can reflect local structural anomalies. The results show that our machine learning model, trained only with data on smaller graphene, can achieve up to 80% prediction accuracy of defects in larger graphene under different practical metrics. The present research sheds light on scalable graphene defect prediction and opens doors for data-driven defect detection for a broad range of two-dimensional materials.

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Machine Learning-Based Detection of Graphene Defects with Atomic Precision

Cited by 24 publications

References 56 publications

Modeling Bioinspired Fish Scale Designs via a Geometric and Numerical Approach

Modeling Bioinspired Fish Scale Designs via a Geometric and Numerical Approach

Learning hidden elasticity with deep neural networks

Scalable Graphene Defect Prediction Using Transferable Learning

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