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RESEARCH PAPER · Science Advances · 2019

Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy study

Maxim A Ziatdinov, Ondrej Dyck, Xin Li, Bobby G. Sumpter, Stephen Jesse, Rama Krishnan Vasudevan, Sergei V. Kalinin

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Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy studyOriginalElectron-Beam Manipulation of Silicon Dopants in Graphene123Lab on a beam—Big data and artificial intelligence in scanning transmission electron microscopy31Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics185Toward Electrochemical Studies on the Nanometer and Atomic Scales: Progress, Challenges, and Opportunities57Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk30Machine learning and big scientific data130Robust multi-scale multi-feature deep learning for atomic and defect identification in Scanning Tunneling Microscopy on H-Si(100) 2x1 surface5Gaussian process analysis of Electron Energy Loss Spectroscopy (EELS) data: parallel reconstruction and kernel control2Deep learning analysis on microscopic imaging in materials science195Reconstruction of the interatomic forces from dynamic scanning transmission electron microscopy data2Machine Learning to Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos198Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data39Automated Searching and Identification of Self-Organized Nanostructures50Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning19Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data2Towards data-driven next-generation transmission electron microscopy248Doping of Cr in Graphene Using Electron Beam Manipulation for Functional Defect Engineering36Doping transition-metal atoms in graphene for atomic-scale tailoring of electronic, magnetic, and quantum topological properties67Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data32Deep learning in electron microscopy103Quantifying Nanoparticle Assembly States in Polymer Matrix Through Deep Learning0Isolated Cobalt Centers on W 18 O 49 Nanowires Perform as a Reaction Switch for Efficient CO 2 Photoreduction311Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration65Hybridized Defects in Solid-State Materials as Artificial Molecules17The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design18Quantifying Nanoparticle Assembly States in a Polymer Matrix through Deep Learning31Steric Hindrance‐ and Work Function‐Promoted High Performance for Electrochemical CO Methanation on Antisite Defects of MoS2 and WS28Exploring order parameters and dynamic processes in disordered systems via variational autoencoders69Quantum Interfaces to the Nanoscale20Imaging Nonradiative Point Defects Buried in Quantum Wells Using Cathodoluminescence57Band gap measurements of monolayer h-BN and insights into carbon-related point defects90In-plane anisotropic electronic properties in layered α′-In2Se35Introduction, production, characterization and applications of defects in graphene69Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy150Quantitative annular dark-field imaging in the scanning transmission electron microscope—a review12The role of convolutional neural networks in scanning probe microscopy: a review39Electronic and magnetic properties of homonuclear and heteronuclear transition metal pairs in graphene6Describing condensed matter from atomically resolved imaging data: from structure to generative and causal models0Morphological Analysis of the Rainbow Patterns Created by Point Defects of Graphene9Gaussian process analysis of electron energy loss spectroscopy data: multivariate reconstruction and kernel control10Atomic-level defect modulation and characterization methods in 2D materials44Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning51Applications of deep learning in electron microscopy72Deep Bayesian local crystallography23In situ transmission electron microscopy and artificial intelligence enabled data analytics for energy materials91Machines for Materials and Materials for Machines: Metal-Insulator Transitions and Artificial Intelligence6Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy44Notes and References0Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning29QUAM-AFM: A Free Database for Molecular Identification by Atomic Force Microscopy19Machine learning in scanning transmission electron microscopy177Machine learning in scanning transmission electron microscopy10Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations10Desirable bonding interactions between organo-functional triazinedithiol groups and heavy metal ions for significantly improved adsorption or dispersion property28Learning motifs and their hierarchies in atomic resolution microscopy31Oxygen vacancy defect engineering to promote catalytic activity toward the oxidation of VOCs: a critical review97Exploring Local Crystal Symmetry with Rotationally Invariant Variational Autoencoders2Emerging machine learning strategies for diminishing measurement uncertainty in SPM nanometrology5From atomically resolved imaging to generative and causal models18The role of temperature on defect diffusion and nanoscale patterning in graphene25Enhancing Infrared Light–Matter Interaction for Deterministic and Tunable Nanomachining of Hexagonal Boron Nitride9Theory+AI/ML for microscopy and spectroscopy: Challenges and opportunities20Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction82AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy133Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns50Machine learning in nanomaterial electron microscopy data analysis13Electron Microscopy Studies of Soft Nanomaterials107Neural network approach for ferroelectric hafnium oxide phase identification at the atomistic scale10Probe microscopy is all you need *20Study of graphene by proton rainbow scattering7Unsupervised machine learning discovery of structural units and transformation pathways from imaging data8Perspectives on ultra-high vacuum transmission electron microscopy of dynamic crystal growth phenomena16Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials21Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy36Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *11Defects go green: using defects in nanomaterials for renewable energy and environmental sustainability10Full automation of point defect detection in transition metal dichalcogenides through a dual mode deep learning algorithm4Co-orchestration of multiple instruments to uncover structure–property relationships in combinatorial libraries11Digital twins and deep learning segmentation of defects in monolayer MX2 phases3Quantitative gas-phase transmission electron microscopy: Where are we now and what comes next?16AI in analytical chemistry: Advancements, challenges, and future directions106Advances and Opportunities in Closed Gas-Cell Transmission Electron Microscopy23Roadmap toward Controlled Ion Beam‐Induced Defects in 2D Materials56Localization and segmentation of atomic columns in supported nanoparticles for fast scanning transmission electron microscopy12Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders26Graphene, beyond lab benches33Edge segmentation method for Si3N4 bearing rolling elements microcracks with profile-distortion4Automated image acquisition and analysis of graphene and hexagonal boron nitride from pristine to highly defective and amorphous structures6Artificial intelligence and deep learning in electron microscopy0Invariant discovery of features across multiple length scales: Applications in microscopy and autonomous materials characterization0Real-space investigations of on-surface intermolecular radical transfer reactions assisted by persistent radicals7AI-assisted wafer-scale exfoliation and transfer of 2D materials: status, challenges and perspectives9Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy6Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning7Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy18Bielectron Vortices in Monolayer Dirac Semimetals1Electron Microscopy Approaches to Unraveling the Structure of Amorphous Materials7Artificial Intelligence Reshaping the Semiconductor Metrology1Electronic, thermoelectric, and optical properties of defective armchair graphene nanoribbons under transverse electric fields0Atomic-scale scanning single-electron transistor microscopy for low-angle twisted two-dimensional heterostructures5Machine learning-assisted advances in graphene and 2D materials0Towards the predictive design of quantum defects for next-generation quantum technologies1From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials1Self-Driving Scanning Probe Microscopy: From Acceleration to Discovery and Manipulation3Straintronics and Twistronics in 2D Materials: Structural Control and Predictive Design0

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105 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Electron-Beam Manipulation of Silicon Dopants in Graphene

    Nano Letters · 2018-06-27

    Mukesh Tripathi, Andreas Mittelberger, Nicholas A. Pike, Clemens Mangler, Jannik C. Meyer, Matthieu Jean Verstraete, Jani Kotakoski, Toma Susi

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  2. Lab on a beam—Big data and artificial intelligence in scanning transmission electron microscopy

    MRS Bulletin · 2019-07-01

    Sergei V. Kalinin, Andrew R. Lupini, Ondrej Dyck, Stephen Jesse, Maxim A Ziatdinov, Rama Krishnan Vasudevan

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  3. Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics

    MRS Communications · 2019-07-22

    Rama Krishnan Vasudevan, Kamal Kumar Choudhary, Apurva Mehta, Ryan P. Smith, Aaron Gilad Kusne, Francesca Tavazza, Lukáš Vlček, Maxim A Ziatdinov, Sergei V. Kalinin, Jason R. Hattrick-Simpers

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  4. Toward Electrochemical Studies on the Nanometer and Atomic Scales: Progress, Challenges, and Opportunities

    ACS Nano · 2019-08-21

    Sergei V. Kalinin, Ondrej Dyck, Nina Balke, Sabine M. Neumayer, Wan‐Yu Tsai, Rama Krishnan Vasudevan, David B. Lingerfelt, Mahshid Ahmadi, Maxim A Ziatdinov, Matthew Todd McDowell, Evgheni Strelcov

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  5. Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk

    Advanced Functional Materials · 2019-11-22

    Maxim Ziatdinov, Ondrej Dyck, Stephen Jesse, Sergei V. Kalinin

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  6. Machine learning and big scientific data

    Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2020-01-20

    Tony Hey, Keith T. Butler, Samuel Jackson, Jeyan Thiyagalingam

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  7. Robust multi-scale multi-feature deep learning for atomic and defect identification in Scanning Tunneling Microscopy on H-Si(100) 2x1 surface

    arXiv (Cornell University) · 2020-02-11

    Maxim A Ziatdinov, Udi Fuchs, James H. G. Owen, John N. Randall, Sergei V. Kalinin

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  8. Gaussian process analysis of Electron Energy Loss Spectroscopy (EELS) data: parallel reconstruction and kernel control

    arXiv (Cornell University) · 2020-05-21

    Sergei V. Kalinin, Andrew R. Lupini, Rama Krishnan Vasudevan, Maxim A Ziatdinov

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  9. Deep learning analysis on microscopic imaging in materials science

    Materials Today Nano · 2020-06-04

    M. Ge, Fei Su, Zhicheng Zhao, D. Su

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  10. Reconstruction of the interatomic forces from dynamic scanning transmission electron microscopy data

    Journal of Applied Physics · 2020-06-08

    M. Chakraborty, M. Ziatdinov, O. Dyck, S. Jesse, A. D. White, Sergei V. Kalinin

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  11. Machine Learning to Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos

    ACS Central Science · 2020-07-06

    Lehan Yao, Zihao Ou, Binbin Luo, Cong Xu, Qian Chen

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  12. Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data

    npj Computational Materials · 2020-08-21

    Maxim A Ziatdinov, Christopher T. Nelson, Xiaohang Zhang, Rama Krishnan Vasudevan, Eugene А. Eliseev, Anna N. Morozovska, Ichiro Takeuchi, Sergei V. Kalinin

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  13. Automated Searching and Identification of Self-Organized Nanostructures

    Nano Letters · 2020-08-31

    Oliver Gordon, Jo E. A. Hodgkinson, Steff Farley, Eugénie L. Hunsicker, Philip Moriarty

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  14. Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning

    Nanotechnology · 2020-09-15

    Maxim A Ziatdinov, Stephen Jesse, Bobby G. Sumpter, Sergei V. Kalinin, Ondrej Dyck

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  15. Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data

    arXiv (Cornell University) · 2020-10-19

    Sergei V. Kalinin, Ondrej Dyck, Ayana Ghosh, Yongtao Liu, Roger B. Proksch, Bobby G. Sumpter, Maxim A Ziatdinov

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  16. Towards data-driven next-generation transmission electron microscopy

    Nature Materials · 2020-10-26

    Steven R. Spurgeon, Colin Ophus, Lewys Jones, Amanda K. Petford-Long, Sergei V. Kalinin, Matthew J. Olszta, Rafal E. Dunin‐Borkowski, Norman J. Salmon, Khalid Hattar, Wei-Chang David Yang, Renu Sharma, Yingge Du, Ann N. Chiaramonti, Haimei Zheng, Edgar C. Buck, Libor Kovařík, R. Lee Penn, Dongsheng Li, Xin Zhang, Mitsuhiro Murayama, Mitra L. Taheri

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  17. Doping of Cr in Graphene Using Electron Beam Manipulation for Functional Defect Engineering

    ACS Applied Nano Materials · 2020-10-28

    Ondrej Dyck, M. Yoon, Lizhi Zhang, Andrew R. Lupini, Jacob L. Swett, Stephen Jesse

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  18. Doping transition-metal atoms in graphene for atomic-scale tailoring of electronic, magnetic, and quantum topological properties

    Carbon · 2020-11-07

    Ondrej Dyck, Lizhi Zhang, M. Yoon, Jacob L. Swett, Dale K. Hensley, Cheng Zhang, Philip D. Rack, Jason D. Fowlkes, Andrew R. Lupini, Stephen Jesse

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  19. Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data

    Nano Letters · 2020-12-11

    Maxim A Ziatdinov, Shuai Zhang, Orion Dollar, Jim Pfaendtner, Christopher J. Mundy, Xin Li, Harley Pyles, David A. Baker, James J. De Yoreo, Sergei V. Kalinin

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  20. Deep learning in electron microscopy

    Machine Learning Science and Technology · 2020-12-22

    Jeffrey M. Ede

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  21. Quantifying Nanoparticle Assembly States in Polymer Matrix Through Deep Learning

    ChemRxiv · 2021-01-05

    Eric Zhonghang Qu, Andrew M. Jimenez, Sanat K. Kumar, Kai Zhang

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  22. Isolated Cobalt Centers on W 18 O 49 Nanowires Perform as a Reaction Switch for Efficient CO 2 Photoreduction

    Journal of the American Chemical Society · 2021-01-28

    Huabin Zhang, Yan Wang, Shouwei Zuo, Wei Zhou, Jing Zhang, Xiong Wen Lou

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  23. Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

    ACS Nano · 2021-02-12

    Seungbum Hong, Chi Hao Liow, Jong Min Yuk, Hye Ryung Byon, Yongsoo Yang, EunAe Cho, Jiwon Yeom, Gun Park, Hyeonmuk Kang, Seung-Gu Kim, Yoonsu Shim, Moony Na, Chaehwa Jeong, Gyuseong Hwang, Hongjun Kim, Hoon Kim, Seongmun Eom, Seongwoo Cho, Hosun Jun, Yongju Lee, Arthur Baucour, Kihoon Bang, Myungjoon Kim, Seokjung Yun, Jeongjae Ryu, Youngjoon Han, Albina Jetybayeva, Pyuck‐Pa Choi, Joshua Agar, Sergei V. Kalinin, Peter W. Voorhees, P. B. Littlewood, Hyuck Mo Lee

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  24. Hybridized Defects in Solid-State Materials as Artificial Molecules

    ACS Nano · 2021-02-18

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  25. The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design

    Life Sciences · 2021-02-22

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  26. Quantifying Nanoparticle Assembly States in a Polymer Matrix through Deep Learning

    Macromolecules · 2021-04-02

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  27. Steric Hindrance‐ and Work Function‐Promoted High Performance for Electrochemical CO Methanation on Antisite Defects of MoS2 and WS2

    ChemSusChem · 2021-04-14

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  28. Exploring order parameters and dynamic processes in disordered systems via variational autoencoders

    Science Advances · 2021-04-21

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  29. Quantum Interfaces to the Nanoscale

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  30. Imaging Nonradiative Point Defects Buried in Quantum Wells Using Cathodoluminescence

    Nano Letters · 2021-06-04

    Thomas F. K. Weatherley, Wei Liu, Vitaly Osokin, Duncan T. L. Alexander, Robert Anthony Taylor, J.‐F. Carlin, R. Butté, N. Grandjean

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  31. Band gap measurements of monolayer h-BN and insights into carbon-related point defects

    2D Materials · 2021-06-22

    Ricardo Javier Peña Román, Fábio J. R. Costa, Alberto Zobelli, Christine Elias, Pierre Valvin, Guillaume Cassabois, Bernard Gil, Alex Summerfield, Tin S. Cheng, Christopher J. Mellor, Peter H. Beton, С. В. Новиков, Luiz Fernando Zagonel

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  32. In-plane anisotropic electronic properties in layered α′-In2Se3

    Journal of Applied Physics · 2021-07-06

    Zhun Liu, Jing Wu, Jingbo Li

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  33. Introduction, production, characterization and applications of defects in graphene

    Journal of Materials Science Materials in Electronics · 2021-07-13

    Waqas Ahmad, Zaka Ullah, Nazmina Imrose Sonil, Karim Khan

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  34. Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy

    ACS Nano · 2021-07-16

    Sergei V. Kalinin, Maxim A Ziatdinov, Jacob D Hinkle, Stephen Jesse, Ayana Ghosh, Kyle P. Kelley, Andrew R. Lupini, Bobby G. Sumpter, Rama Krishnan Vasudevan

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  35. Quantitative annular dark-field imaging in the scanning transmission electron microscope—a review

    Journal of Physics Materials · 2021-08-04

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  36. The role of convolutional neural networks in scanning probe microscopy: a review

    Beilstein Journal of Nanotechnology · 2021-08-13

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  37. Electronic and magnetic properties of homonuclear and heteronuclear transition metal pairs in graphene

    Applied Surface Science · 2021-08-19

    Mahesh Datt Bhatt, Gunn Hee Kim

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  38. Describing condensed matter from atomically resolved imaging data: from structure to generative and causal models

    arXiv (Cornell University) · 2021-09-15

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

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

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  40. Gaussian process analysis of electron energy loss spectroscopy data: multivariate reconstruction and kernel control

    npj Computational Materials · 2021-09-28

    Sergei V. Kalinin, Andrew R. Lupini, Rama Krishnan Vasudevan, Maxim A Ziatdinov

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  41. Atomic-level defect modulation and characterization methods in 2D materials

    APL Materials · 2021-10-01

    Odongo Francis Ngome Okello, Dong‐Hwan Yang, Yuseong Chu, Sejung Yang, Si‐Young Choi

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  42. Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning

    Nature Communications · 2021-10-29

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  43. Applications of deep learning in electron microscopy

    Microscopy · 2021-11-08

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  44. Deep Bayesian local crystallography

    npj Computational Materials · 2021-11-10

    Sergei V. Kalinin, Mark P. Oxley, Mani Valleti, Junjie Zhang, Raphaël P. Hermann, Hong Zheng, Wenrui Zhang, Gyula Eres, Rama Krishnan Vasudevan, Maxim A Ziatdinov

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  45. In situ transmission electron microscopy and artificial intelligence enabled data analytics for energy materials

    Journal of Energy Chemistry · 2021-12-08

    Hongkui Zheng, Xiner Lu, Kai He

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  46. Machines for Materials and Materials for Machines: Metal-Insulator Transitions and Artificial Intelligence

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  47. Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy

    IEEE Transactions on Computational Imaging · 2022-01-01

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  48. Notes and References

    Elsevier eBooks · 2022-01-01

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  49. Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

    Nanoscale · 2022-01-01

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  50. QUAM-AFM: A Free Database for Molecular Identification by Atomic Force Microscopy

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