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RESEARCH PAPER · Nano-Micro Letters · 2020

Machine Learning-Based Detection of Graphene Defects with Atomic Precision

Bowen Zheng, Grace X. Gu

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Machine Learning-Based Detection of Graphene Defects with Atomic PrecisionOriginalPhysics-informed deep learning for digital materials74DUPLICATE: Physics-informed deep learning for digital materials0Prediction of Graphene Oxide Functionalization Using Gradient Boosting: Implications for Material Chemical Composition Identification14Modeling and simulation of the mechanical properties of graphene — A comprehensive review2Reversible Lectin Binding to Glycan-Functionalized Graphene3Learning hidden elasticity with deep neural networks86Scalable Graphene Defect Prediction Using Transferable Learning7Morphological Analysis of the Rainbow Patterns Created by Point Defects of Graphene9Modeling Bioinspired Fish Scale Designs via a Geometric and Numerical Approach13Defect recognition in line-space patterns aided by deep learning with data augmentation4Uncertainty quantification and prediction for mechanical properties of graphene aerogels via Gaussian process metamodels7Machine learning for accelerating the design process of double-double composite structures84Recent Advances of Graphene and Related Materials in Artificial Intelligence43A State-of-the-Art Review on Machine Learning-Based Multiscale Modeling, Simulation, Homogenization and Design of Materials222Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning102Fracture strength of Graphene at high temperatures: data driven investigations supported by MD and analytical approaches16Designing mechanically tough graphene oxide materials using deep reinforcement learning33Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics141Machine learning application in evaluation of graphite plates0Study of graphene by proton rainbow scattering7Using 3D printing as a research tool for materials discovery12Dual Supports by Cation Vacancies and Surface Optimization for CoNiSe 2 -Based Hybrid Supercapacitors with High Energy Density82Biomass-Derived Carbonaceous Materials with Graphene/Graphene-Like Structures: Definition, Classification, and Environmental Applications41An adaptive machine learning-based optimization method in the aerodynamic analysis of a finite wing under various cruise conditions16When Machine Learning Meets 2D Materials: A Review156Machine learning and molecular dynamics0Machine learning with MD0Graphene Applications in Composites, Energy, and Water Treatment18A Comprehensive Review of Quality Control and Reliability Research in Micro–Nano Technology59Machine learning-driven material intelligence research and development14Structural optimization of magnetic flux concentrator based on particle swarm optimization algorithm0Multiscale Modeling Techniques in the Study of 2-Dimensional Materials under Ionizing Radiation2Introduction0Multiscale modeling of graphene and carbon nanostructures: advances in atomistic, coarse-grained, and machine learning approaches4AI-Driven Modeling and Optimization of 3D Graphene–MXene Hybrid Nanocomposites0Physicochemical Insights into Laser-Induced Graphene via Data-Driven Materials-Genome Analysis of Natural and Synthetic Precursors0

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    Theoretical and Applied Mechanics Letters · 2021-01-01

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  2. DUPLICATE: Physics-informed deep learning for digital materials

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  3. Prediction of Graphene Oxide Functionalization Using Gradient Boosting: Implications for Material Chemical Composition Identification

    ACS Applied Nano Materials · 2021-02-23

    Bowen Zheng, Grace X. Gu

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  4. Modeling and simulation of the mechanical properties of graphene — A comprehensive review

    International Journal of Computational Materials Science and Engineering · 2021-04-06

    Teng Yong Ng, William Toh

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  5. Reversible Lectin Binding to Glycan-Functionalized Graphene

    International Journal of Molecular Sciences · 2021-06-22

    Tereza Koukalová, Petr Kovaříček, Pavla Bojarová, Valentino L. P. Guerra, Vladimír Vrkoslav, Lukáš Navara, Ivan Jirka, Marek Cebecauer, Vladimı́r Křen, Martin Kalbáč

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  6. Learning hidden elasticity with deep neural networks

    Proceedings of the National Academy of Sciences · 2021-07-29

    Chun‐Teh Chen, Grace X. Gu

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  7. Scalable Graphene Defect Prediction Using Transferable Learning

    Nanomaterials · 2021-09-09

    Bowen Zheng, Zeyu Zheng, Grace X. Gu

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  8. Morphological Analysis of the Rainbow Patterns Created by Point Defects of Graphene

    The Journal of Physical Chemistry C · 2021-09-15

    M. Haždijojić, M. Ćosić, Ruslan A. Rymzhanov

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  9. Modeling Bioinspired Fish Scale Designs via a Geometric and Numerical Approach

    Materials · 2021-09-17

    Ailin Chen, Komal Thind, Kahraman G. Demir, Grace X. Gu

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  10. Defect recognition in line-space patterns aided by deep learning with data augmentation

    Journal of Micro/Nanopatterning Materials and Metrology · 2021-09-24

    Jihun Ahn, Ye Chan Kim, So Youn Kim, Su‐Mi Hur, Vikram Thapar

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  11. Uncertainty quantification and prediction for mechanical properties of graphene aerogels via Gaussian process metamodels

    Nano Futures · 2021-11-23

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  12. Machine learning for accelerating the design process of double-double composite structures

    Composite Structures · 2022-01-10

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  13. Recent Advances of Graphene and Related Materials in Artificial Intelligence

    Advanced Intelligent Systems · 2022-06-26

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  14. A State-of-the-Art Review on Machine Learning-Based Multiscale Modeling, Simulation, Homogenization and Design of Materials

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    Xueye Chen, Honglin Lv

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  16. Fracture strength of Graphene at high temperatures: data driven investigations supported by MD and analytical approaches

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    S. D. V. S. S. Varma Siruvuri, Hritik Verma, Brahmanandam Javvaji, P. R. Budarapu

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  17. Designing mechanically tough graphene oxide materials using deep reinforcement learning

    npj Computational Materials · 2022-11-05

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  18. Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

    Nanoscale Advances · 2022-11-07

    Hong Zhou, Liangge Xu, Zhihao Ren, Jiaqi Zhu, Chengkuo Lee

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  19. Machine learning application in evaluation of graphite plates

    AIP conference proceedings · 2023-01-01

    Ram Krishna, Syed Akram, Panchangam Madhuri, T. V. V. L. N. Rao

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  20. Study of graphene by proton rainbow scattering

    The European Physical Journal D · 2023-05-01

    M. Hadžijojić, M. Ćosić

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  21. Using 3D printing as a research tool for materials discovery

    Device · 2023-07-01

    Ronald A. Smaldone, Keith A. Brown, Grace X. Gu, Chenfeng Ke

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  22. Dual Supports by Cation Vacancies and Surface Optimization for CoNiSe 2 -Based Hybrid Supercapacitors with High Energy Density

    ACS Energy Letters · 2023-07-18

    Qingjie Lu, Tong Zhou, Baoye Zi, Jianhong Zhao, Dequan Li, Mingpeng Chen, Huachuan Sun, Jin Zhang, Yumin Zhang, Qingju Liu

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  23. Biomass-Derived Carbonaceous Materials with Graphene/Graphene-Like Structures: Definition, Classification, and Environmental Applications

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    Shijie Yuan, Jing-Jing Wang, Bin Dong, Xiaohu Dai

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  24. An adaptive machine learning-based optimization method in the aerodynamic analysis of a finite wing under various cruise conditions

    Theoretical and Applied Mechanics Letters · 2023-12-14

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  25. When Machine Learning Meets 2D Materials: A Review

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    Bin Lu, Yuze Xia, Yuqian Ren, Xie Miaomiao, Liguo Zhou, Giovanni Vinai, Simon A. Morton, Andrew T. S. Wee, Wilfred G. van der Wiel, Wen Zhang, Ping Kwan Johnny Wong

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  26. Machine learning and molecular dynamics

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  27. Machine learning with MD

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  28. Graphene Applications in Composites, Energy, and Water Treatment

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  30. Machine learning-driven material intelligence research and development

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  31. Structural optimization of magnetic flux concentrator based on particle swarm optimization algorithm

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  32. Multiscale Modeling Techniques in the Study of 2-Dimensional Materials under Ionizing Radiation

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