Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

Ziatdinov, Maxim; Dyck, Ondrej; Maksov, Artem; Li, Xufan; Sang, Xiahan; Xiao, Kai; Unocic, Raymond R.; Vasudevan, Rama; Jesse, Stephen; Kalinin, Sergei V.

doi:10.1021/acsnano.7b07504

Title: Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

Journal Article · Thu Dec 07 00:00:00 EST 2017 · ACS Nano

DOI:https://doi.org/10.1021/acsnano.7b07504· OSTI ID:1427646

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS) and Inst. for Functional Imaging of Materials
Univ. of Tennessee, Knoxville, TN (United States). Bredesen Center for Interdisciplinary Research and Graduate Education; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS) and Inst. for Functional Imaging of Materials
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)

Recent advances in scanning transmission electron and scanning probe microscopies have opened unprecedented opportunities in probing the materials structural parameters and various functional properties in real space with an angstrom-level precision. This progress has been accompanied by exponential increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large datasets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. Here we demonstrate an application of deep neural networks to extracting information from atomically resolved images including location of the atomic species and type of defects. We develop a “weakly-supervised” approach that uses information on the coordinates of all atomic species in the image, extracted via a deep neural network, to identify a rich variety of defects that are not part of an initial training set. We further apply our approach to interpret complex atomic and defect transformation, including switching between different coordination of silicon dopants in graphene as a function of time, formation of peculiar silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of molecular “rotor”. In conclusion, this deep learning based approach resembles logic of a human operator, but can be scaled leading to significant shift in the way of extracting and analyzing information from raw experimental data.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1427646

Journal Information:: ACS Nano, Vol. 11, Issue 12; ISSN 1936-0851

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 193 works

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References (41)

Atomic-Level Sculpting of Crystalline Oxides: Toward Bulk Nanofabrication with Single Atomic Plane Precision Jesse, Stephen; He, Qian; Lupini, Andrew R. Small, Vol. 11, Issue 44 https://doi.org/10.1002/smll.201502048	journal	October 2015
Machine learning approaches in medical image analysis: From detection to diagnosis de Bruijne, Marleen Medical Image Analysis, Vol. 33 https://doi.org/10.1016/j.media.2016.06.032	journal	October 2016
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Computational insights and the observation of SiC nanograin assembly: towards 2D silicon carbide Susi, Toma; Skákalová, Viera; Mittelberger, Andreas Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-04683-9	journal	June 2017
Oxygen-Vacancy-Induced Polar Behavior in (LaFeO ₃ ) ₂ /(SrFeO ₃ ) Superlattices Mishra, Rohan; Kim, Young-Min; Salafranca, Juan Nano Letters, Vol. 14, Issue 5 https://doi.org/10.1021/nl500601d	journal	April 2014
Observing Atomic Collapse Resonances in Artificial Nuclei on Graphene Wang, Y.; Wong, D.; Shytov, A. V. Science, Vol. 340, Issue 6133 https://doi.org/10.1126/science.1234320	journal	March 2013
The perceptron: A probabilistic model for information storage and organization in the brain. Rosenblatt, F. Psychological Review, Vol. 65, Issue 6 https://doi.org/10.1037/h0042519	journal	January 1958
Fire up the atom forge Kalinin, Sergei V.; Borisevich, Albina; Jesse, Stephen Nature, Vol. 539, Issue 7630 https://doi.org/10.1038/539485a	journal	November 2016
Lateral Hopping of Molecules Induced by Excitation of Internal Vibration Mode Komeda, T. Science, Vol. 295, Issue 5562 https://doi.org/10.1126/science.1069016	journal	March 2002
Interplay of chemical disorder and electronic inhomogeneity in unconventional superconductors Zeljkovic, Ilija; Hoffman, Jennifer E. Physical Chemistry Chemical Physics, Vol. 15, Issue 32 https://doi.org/10.1039/c3cp51387d	journal	January 2013
Complex structural dynamics of nanocatalysts revealed in Operando conditions by correlated imaging and spectroscopy probes Li, Y.; Zakharov, D.; Zhao, S. Nature Communications, Vol. 6, Issue 1 https://doi.org/10.1038/ncomms8583	journal	June 2015
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Badrinarayanan, Vijay; Kendall, Alex; Cipolla, Roberto IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, Issue 12 https://doi.org/10.1109/TPAMI.2016.2644615	journal	December 2017
Isoelectronic Tungsten Doping in Monolayer MoSe ₂ for Carrier Type Modulation Li, Xufan; Lin, Ming-Wei; Basile, Leonardo Advanced Materials, Vol. 28, Issue 37 https://doi.org/10.1002/adma.201601991	journal	July 2016
Effective Cleaning of Hexagonal Boron Nitride for Graphene Devices Garcia, Andrei G. F.; Neumann, Michael; Amet, François Nano Letters, Vol. 12, Issue 9 https://doi.org/10.1021/nl3011726	journal	January 2012
Scanning Tunneling Spectroscopy Zandvliet, Harold J. W.; van Houselt, Arie Annual Review of Analytical Chemistry, Vol. 2, Issue 1 https://doi.org/10.1146/annurev-anchem-060908-155213	journal	July 2009
Single-atom fabrication with electron and ion beams: From surfaces and two-dimensional materials toward three-dimensional atom-by-atom assembly Kalinin, Sergei V.; Pennycook, Stephen J. MRS Bulletin, Vol. 42, Issue 09 https://doi.org/10.1557/mrs.2017.186	journal	September 2017
Learning phase transitions by confusion van Nieuwenburg, Evert P. L.; Liu, Ye-Hua; Huber, Sebastian D. Nature Physics, Vol. 13, Issue 5 https://doi.org/10.1038/nphys4037	journal	February 2017
Machine learning phases of matter Carrasquilla, Juan; Melko, Roger G. Nature Physics, Vol. 13, Issue 5 https://doi.org/10.1038/nphys4035	journal	February 2017
Large scale atmospheric pressure chemical vapor deposition of graphene Vlassiouk, Ivan; Fulvio, Pasquale; Meyer, Harry Carbon, Vol. 54 https://doi.org/10.1016/j.carbon.2012.11.003	journal	April 2013
Structural Defects in Graphene Banhart, Florian; Kotakoski, Jani; Krasheninnikov, Arkady V. ACS Nano, Vol. 5, Issue 1 https://doi.org/10.1021/nn102598m	journal	November 2010
Atom-by-atom structural and chemical analysis by annular dark-field electron microscopy Krivanek, Ondrej L.; Chisholm, Matthew F.; Nicolosi, Valeria Nature, Vol. 464, Issue 7288 https://doi.org/10.1038/nature08879	journal	March 2010
Machine learning in cell biology – teaching computers to recognize phenotypes Sommer, Christoph; Gerlich, Daniel W. Journal of Cell Science, Vol. 126, Issue 24 https://doi.org/10.1242/jcs.123604	journal	November 2013
Direct Imaging of Covalent Bond Structure in Single-Molecule Chemical Reactions de Oteyza, D. G.; Gorman, P.; Chen, Y. -C. Science, Vol. 340, Issue 6139 https://doi.org/10.1126/science.1238187	journal	May 2013
Placing single atoms in graphene with a scanning transmission electron microscope Dyck, Ondrej; Kim, Songkil; Kalinin, Sergei V. Applied Physics Letters, Vol. 111, Issue 11 https://doi.org/10.1063/1.4998599	journal	September 2017
Surface faceting and elemental diffusion behaviour at atomic scale for alloy nanoparticles during in situ annealing Chi, Miaofang; Wang, Chao; Lei, Yinkai Nature Communications, Vol. 6, Issue 1 https://doi.org/10.1038/ncomms9925	journal	November 2015
Observation of polar vortices in oxide superlattices Yadav, A. K.; Nelson, C. T.; Hsu, S. L. Nature, Vol. 530, Issue 7589 https://doi.org/10.1038/nature16463	journal	January 2016
Controllable shrinking and shaping of silicon nitride nanopores under electron irradiation Zhang, W. M.; Wang, Y. G.; Li, J. Applied Physics Letters, Vol. 90, Issue 16 https://doi.org/10.1063/1.2723680	journal	April 2007
Orbital Occupancy and Charge Doping in Iron-Based Superconductors Cantoni, Claudia; Mitchell, Jonathan E.; May, Andrew F. Advanced Materials, Vol. 26, Issue 35 https://doi.org/10.1002/adma.201401518	journal	July 2014
Picometre-precision analysis of scanning transmission electron microscopy images of platinum nanocatalysts Yankovich, Andrew B.; Berkels, Benjamin; Dahmen, W. Nature Communications, Vol. 5, Issue 1 https://doi.org/10.1038/ncomms5155	journal	June 2014
Direct Determination of the Chemical Bonding of Individual Impurities in Graphene Zhou, Wu; Kapetanakis, Myron D.; Prange, Micah P. Physical Review Letters, Vol. 109, Issue 20 https://doi.org/10.1103/PhysRevLett.109.206803	journal	November 2012
Fully Convolutional Networks for Semantic Segmentation Shelhamer, Evan; Long, Jonathan; Darrell, Trevor IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, Issue 4 https://doi.org/10.1109/TPAMI.2016.2572683	journal	April 2017
Direct Observation of Dopant Atom Diffusion in a Bulk Semiconductor Crystal Enhanced by a Large Size Mismatch Ishikawa, Ryo; Mishra, Rohan; Lupini, Andrew R. Physical Review Letters, Vol. 113, Issue 15 https://doi.org/10.1103/PhysRevLett.113.155501	journal	October 2014
Functional and Spectroscopic Measurements with Scanning Tunneling Microscopy Moore, Amanda M.; Weiss, Paul S. Annual Review of Analytical Chemistry, Vol. 1, Issue 1 https://doi.org/10.1146/annurev.anchem.1.031207.112932	journal	July 2008
Suppression of Defects and Deep Levels Using Isoelectronic Tungsten Substitution in Monolayer MoSe ₂ Li, Xufan; Puretzky, Alexander A.; Sang, Xiahan Advanced Functional Materials, Vol. 27, Issue 19 https://doi.org/10.1002/adfm.201603850	journal	October 2016
Searching for exotic particles in high-energy physics with deep learning Baldi, P.; Sadowski, P.; Whiteson, D. Nature Communications, Vol. 5, Issue 1 https://doi.org/10.1038/ncomms5308	journal	July 2014
Identifying local structural states in atomic imaging by computer vision Laanait, Nouamane; Ziatdinov, Maxim; He, Qian Advanced Structural and Chemical Imaging, Vol. 2, Issue 1 https://doi.org/10.1186/s40679-016-0028-8	journal	November 2016
Direct Observation of Atomic Dynamics and Silicon Doping at a Topological Defect in Graphene Yang, Zhiqing; Yin, Lichang; Lee, Jaekwang Angewandte Chemie, Vol. 126, Issue 34 https://doi.org/10.1002/ange.201403382	journal	June 2014
Mechanochemistry Induced Using Force Exerted by a Functionalized Microscope Tip Zhang, Yajie; Wang, Yongfeng; Lü, Jing-Tao Angewandte Chemie International Edition, Vol. 56, Issue 39 https://doi.org/10.1002/anie.201704940	journal	August 2017
Structure and bonding at the atomic scale by scanning transmission electron microscopy Muller, David A. Nature Materials, Vol. 8, Issue 4 https://doi.org/10.1038/nmat2380	journal	April 2009
Probing the Bonding and Electronic Structure of Single Atom Dopants in Graphene with Electron Energy Loss Spectroscopy Ramasse, Quentin M.; Seabourne, Che R.; Kepaptsoglou, Despoina-Maria Nano Letters, Vol. 13, Issue 10 https://doi.org/10.1021/nl304187e	journal	January 2013
Mitigating e-beam-induced hydrocarbon deposition on graphene for atomic-scale scanning transmission electron microscopy studies Dyck, Ondrej; Kim, Songkil; Kalinin, Sergei V. Journal of Vacuum Science & Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phenomena, Vol. 36, Issue 1 https://doi.org/10.1116/1.5003034	journal	January 2018

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Title: Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

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