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RESEARCH PAPER · npj Computational Materials · 2018

Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images

Rama Krishnan Vasudevan, Nouamane Laanait, Erik M. Ferragut, Kai Wang, David B. Geohegan, Kai Xiao, Maxim A Ziatdinov, Stephen Jesse, Ondrej Dyck, Sergei V. Kalinin

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Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved imagesOriginalOn Classification Approaches for Crystallographic Symmetries of Noisy 2D Periodic Patterns17Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2168Atom-by-atom fabrication with electron beams128Toward Electrochemical Studies on the Nanometer and Atomic Scales: Progress, Challenges, and Opportunities57A self-driving microscope and the Atomic Forge20Exascale Deep Learning for Scientific Inverse Problems27From representation learning to thematic classification - Application to hierarchical analysis of hyperspectral images0Automated Classification of Helium Ingress in Irradiated X-7504Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy9Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images8Deep learning of interface structures from the 4D STEM data: cation intermixing vs. roughening1Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99The importance of temporal and spatial incoherence in quantitative interpretation of 4D-STEM12Deep learning analysis on microscopic imaging in materials science195Automated Detection of Helium Bubbles in Irradiated X-75048Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening ∗8From Ionic Surfactants to Nafion through Convolutional Neural Networks14Machine learning in materials design: Algorithm and application*62Doping 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 properties67Emerging materials intelligence ecosystems propelled by machine learning441Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning51Deep learning in electron microscopy103Automated Processing of Environmental Transmission Electron Microscopy Images for Quantification of Thin Film Dewetting and Carbon Nanotube Nucleation Dynamics8Computational scanning tunneling microscope image database41A deep learning based automatic defect analysis framework for In-situ TEM ion irradiations75Objective crystallographic symmetry classifications of noisy and noise-free 2D periodic patterns with strong Fedorov type pseudosymmetries0Neural network-based order parameter for phase transitions and its applications in high-entropy alloys49Machine learning for multiscale modeling in computational molecular design25Defect detection in atomic-resolution images via unsupervised learning with translational invariance29In situ transmission electron microscopy and artificial intelligence enabled data analytics for energy materials91Site‐Specific Regulated Memristors via Electron‐Beam‐Induced Functionalization of HfO223Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook134Design of a graphical user interface for few-shot machine learning classification of electron microscopy data25Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning127Learning motifs and their hierarchies in atomic resolution microscopy31Bringing into play automated electron microscopy data processing for understanding nanoparticulate electrocatalysts’ structure–property relationships10Detection of zirconium hydrides in transmission electron micrographs using deep neural networks4Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression33SIGMA: Spectral Interpretation Using Gaussian Mixtures and Autoencoder11Predicting thermodynamic stability of magnesium alloys in machine learning33Deep learning modeling in microscopy imaging: A review of materials science applications62Detecting and Correcting Piezoelectric-tube Actuator Drift Induced Distortion in Atomic-Resolution Scanning Tunneling Microscope Images from Crystal Surfaces3Deep 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 microscopy36Predicting column heights and elemental composition in experimental transmission electron microscopy images of high-entropy oxides using deep learning13Atom Identification in Bilayer Moiré Materials with Gomb-Net2Two-Stage Machine Learning Framework for Accurate Discrimination of Isomers and Very-Similar Molecules on Surfaces10In-situ TEM observation of the seed-free growth of five-fold twinned Au decahedra3

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  1. On Classification Approaches for Crystallographic Symmetries of Noisy 2D Periodic Patterns

    IEEE Transactions on Nanotechnology · 2019-01-01

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  2. Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2

    npj Computational Materials · 2019-02-01

    Artem Maksov, Ondrej Dyck, Kai Wang, Kai Xiao, David B. Geohegan, Bobby G. Sumpter, Rama K. Vasudevan, Stephen Jesse, Sergei V. Kalinin, Maxim A Ziatdinov

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  3. Atom-by-atom fabrication with electron beams

    Nature Reviews Materials · 2019-06-07

    Ondrej Dyck, Maxim A Ziatdinov, David B. Lingerfelt, Raymond R. Unocic, Bethany M. Hudak, Andrew R. Lupini, Stephen Jesse, Sergei V. Kalinin

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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. A self-driving microscope and the Atomic Forge

    MRS Bulletin · 2019-09-01

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  6. Exascale Deep Learning for Scientific Inverse Problems

    arXiv (Cornell University) · 2019-09-24

    Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Y. Borisevich, Sergeev, Alex, Michael A. Matheson

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  7. From representation learning to thematic classification - Application to hierarchical analysis of hyperspectral images

    Unknown journal · 2019-11-06

    Adrien Lagrange

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  8. Automated Classification of Helium Ingress in Irradiated X-750

    arXiv (Cornell University) · 2019-12-09

    Anderson, Chris, Jacob Klein, Heygaan Rajakumar, C. D. Judge, Laurent Karim Béland

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  9. Machine Learning for Catalysis Informatics: Recent Applications and Prospects

    ACS Catalysis · 2019-12-16

    Takashi Toyao, Zen Maeno, Satoru Takakusagi, Takashi Kamachi, Ichigaku Takigawa, Ken‐ichi Shimizu

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  10. Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy

    arXiv (Cornell University) · 2019-12-19

    Kamal Kumar Choudhary, Kevin F. Garrity, Charles H. Camp, Sergei V. Kalinin, Rama Krishnan Vasudevan, Maxim A Ziatdinov, Francesca Tavazza

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  11. Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images

    Open Archive Toulouse Archive Ouverte (University of Toulouse) · 2020-02-01

    Adrien Lagrange, Mathieu Fauvel, Stéphane May, Nicolas Dobigeon

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  12. Deep learning of interface structures from the 4D STEM data: cation intermixing vs. roughening

    arXiv (Cornell University) · 2020-02-20

    Mark P. Oxley, Junqi Yin, Nikolay Borodinov, Suhas Somnath, Maxim A Ziatdinov, Andrew R. Lupini, Stephen Jesse, Rama Krishnan Vasudevan, Sergei V. Kalinin

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  13. Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery

    Annual Review of Materials Research · 2020-04-21

    Changwon Suh, Clyde Fare, James A. Warren, Edward O. Pyzer‐Knapp

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  14. The importance of temporal and spatial incoherence in quantitative interpretation of 4D-STEM

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    Mark P. Oxley, Ondrej Dyck

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  15. 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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  16. Automated Detection of Helium Bubbles in Irradiated X-750

    Ultramicroscopy · 2020-07-02

    Chris M. Anderson, Jacob Klein, Heygaan Rajakumar, C. D. Judge, Laurent Karim Béland

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  17. Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening ∗

    Machine Learning Science and Technology · 2020-07-06

    Mark P. Oxley, Jia‐Xin Yin, Nikolay Borodinov, Suhas Somnath, Maxim A Ziatdinov, Andrew R. Lupini, Stephen Jesse, Rama Krishnan Vasudevan, S.V. Kalinin

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  18. From Ionic Surfactants to Nafion through Convolutional Neural Networks

    The Journal of Physical Chemistry B · 2020-09-18

    Loïc Dumortier, Stefano Mossa

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  19. Machine learning in materials design: Algorithm and application*

    Chinese Physics B · 2020-10-14

    Zhilong Song, Xiwen Chen, Fanbin Meng, Guanjian Cheng, Chen Wang, Zhongti Sun, Wan‐Jian Yin

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  20. 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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  21. 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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  22. Emerging materials intelligence ecosystems propelled by machine learning

    Nature Reviews Materials · 2020-11-09

    Rohit Batra, Le Song, Rampi Ramprasad

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  23. Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning

    npj Computational Materials · 2020-12-17

    Leslie Ching Ow Tiong, Kim Jeongrae, Sang Soo Han, Dong-Hun Kim

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  24. Deep learning in electron microscopy

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  25. Automated Processing of Environmental Transmission Electron Microscopy Images for Quantification of Thin Film Dewetting and Carbon Nanotube Nucleation Dynamics

    Carbon · 2021-01-01

    Nicholas T. Dee, Martin Schneider, Dmitri N. Zakharov, Piran R. Kidambi, A. John Hart

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  26. Computational scanning tunneling microscope image database

    Scientific Data · 2021-02-11

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  27. A deep learning based automatic defect analysis framework for In-situ TEM ion irradiations

    Computational Materials Science · 2021-05-19

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  28. Objective crystallographic symmetry classifications of noisy and noise-free 2D periodic patterns with strong Fedorov type pseudosymmetries

    arXiv (Cornell University) · 2021-07-22

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  29. Neural network-based order parameter for phase transitions and its applications in high-entropy alloys

    Nature Computational Science · 2021-10-18

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  30. Machine learning for multiscale modeling in computational molecular design

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  31. Defect detection in atomic-resolution images via unsupervised learning with translational invariance

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

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  33. Site‐Specific Regulated Memristors via Electron‐Beam‐Induced Functionalization of HfO2

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  37. Learning motifs and their hierarchies in atomic resolution microscopy

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  38. Bringing into play automated electron microscopy data processing for understanding nanoparticulate electrocatalysts’ structure–property relationships

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  39. Detection of zirconium hydrides in transmission electron micrographs using deep neural networks

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  40. Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression

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  41. SIGMA: Spectral Interpretation Using Gaussian Mixtures and Autoencoder

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  42. Predicting thermodynamic stability of magnesium alloys in machine learning

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  47. Predicting column heights and elemental composition in experimental transmission electron microscopy images of high-entropy oxides using deep learning

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