Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-26T21:18:34.809Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  12 July 2019

Sergei V. Kalinin ,
Andrew R. Lupini ,
Ondrej Dyck ,
Stephen Jesse ,
Maxim Ziatdinov  and
Rama K. Vasudevan
Sergei V. Kalinin
Affiliation:
The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, USA; sergei2@ornl.gov
Andrew R. Lupini
Affiliation:
The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, USA; arl1000@ornl.gov
Ondrej Dyck
Affiliation:
The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, USA; dyckoe@ornl.gov
Stephen Jesse
Affiliation:
The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, USA; sjesse@ornl.gov
Maxim Ziatdinov
Affiliation:
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, USA; ziatdinovma@ornl.gov
Rama K. Vasudevan
Affiliation:
The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, USA; vasudevanrk@ornl.gov
Get access

Abstract

Atomically resolved imaging of materials enabled by the advent of aberration-corrected scanning transmission electron microscopy (STEM) has become a mainstay of modern materials science. However, much of the wealth of quantitative information contained in the fine details of atomic structure or spectra remains largely unexplored. In this article, we discuss new opportunities enabled by physics-informed big data and machine learning technologies to extract physical information from static and dynamic STEM images, ranging from statistical thermodynamics of alloys to kinetics of solid-state reactions at a single defect level. The synergy of deep-learning image analytics and real-time feedback further allows harnessing beam-induced atomic and bond dynamics to enable direct atom-by-atom fabrication. Examples of direct atomic motion over mesoscopic distances, engineered doping at selected lattice sites, and assembly of multiatomic structures are reviewed. These advances position the scanning transmission electron microscope to transition from a mere imaging tool toward a complete nanoscale laboratory for exploring electronic, phonon, and quantum phenomena in atomically engineered structures.

Keywords

scanning transmission electron microscopy (STEM)interatomic arrangementscrystallographic structureelectronic structure
Type
Technical Feature
Information
MRS Bulletin , Volume 44 , Issue 7: The Machine Learning Revolution in Materials Research , July 2019 , pp. 565 - 575
Copyright
Copyright © Materials Research Society 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This article is based on the Symposium X (Frontiers of Materials Research) presentation given at the 2018 MRS Fall Meeting in Boston, Mass.

References

Martin, J.D., Solid State Insurrection: How the Science of Substance Made American Physics Matter (University of Pittsburgh Press, Pittsburgh, 2018).CrossRefGoogle Scholar
Mody, C.C.M., Instrumental Community (MIT Press, Cambridge, MA, 2011).CrossRefGoogle Scholar
Krivanek, O.L., Dellby, N., Spence, A.J., Camps, R.A., Brown, L.M., “Aberration Correction in the STEM,” in Electron Microscopy and Analysis 1997, Rodenburg, J.M., Ed. (IOP Publishing, Bristol, 1997), p. 35.Google Scholar
Haider, M., Uhlemann, S., Schwan, E., Rose, H., Kabius, B., Urban, K., Nature 392, 768 (1998).CrossRefGoogle Scholar
Scherzer, O., Optik 2, 114 (1947).Google Scholar
Dellby, N., Krivanek, O.L., Nellist, P.D., Batson, P.E., Lupini, A.R., J. Electron Microsc. 50, 177 (2001).Google Scholar
Pennycook, S.J., Nellist, P.D., Eds., Scanning Transmission Electron Microscopy: Imaging and Analysis (Springer, New York, 2011).CrossRefGoogle Scholar
Varela, M., Findlay, S.D., Lupini, A.R., Christen, H.M., Borisevich, A.Y., Dellby, N., Krivanek, O.L., Nellist, P.D., Oxley, M.P., Allen, L.J., Pennycook, S.J., Phys. Rev. Lett. 92, 095502 (2004).CrossRefGoogle Scholar
Idrobo, J.C., Lupini, A.R., Feng, T.L., Unocic, R.R., Walden, F.S., Gardiner, D.S., Lovejoy, T.C., Dellby, N., Pantelides, S.T., Krivanek, O.L., Phys. Rev. Lett. 120, 095901 (2018).CrossRefGoogle Scholar
Larocque, H., Bouchard, F., Grillo, V., Sit, A., Frabboni, S., Dunin-Borkowski, R.E., Padgett, M.J., Boyd, R.W., Karimi, E., Phys. Rev. Lett. 117, 154801 (2016).CrossRefGoogle Scholar
Jiang, Y., Chen, Z., Hang, Y.M., Deb, P., Gao, H., Xie, S.E., Purohit, P., Tate, M.W., Park, J., Gruner, S.M., Elser, V., Muller, D.A., Nature 559, 343 (2018).CrossRefGoogle Scholar
Dyck, O., Kim, S., Jimenez-Izal, E., Alexandrova, A.N., Kalinin, S.V., Jesse, S., Small 14, e1801771 (2018).CrossRefGoogle Scholar
Susi, T., Meyer, J.C., Kotakoski, J., Ultramicroscopy 180, 163 (2017).CrossRefGoogle Scholar
Eigler, D.M., Schweizer, E.K., Nature 344, 524 (1990).CrossRefGoogle Scholar
Pennycook, S.J., Kalinin, S.V., Nature 515, 487 (2014).CrossRefGoogle Scholar
Ziatdinov, M., Dyck, O., Jesse, S., Kalinin, S.V., “Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk,” arXiv preprint arXiv:09322 (2019).Google Scholar
Jesse, S., He, Q., Lupini, A.R., Leonard, D.N., Oxley, M.P., Ovchinnikov, O., Unocic, R.R., Tselev, A., Fuentes-Cabrera, M., Sumpter, B.G., Pennycook, S.J., Kalinin, S.V., Borisevich, A.Y., Small 11, 5895 (2015).CrossRefGoogle Scholar
Feng, J., Kvit, A.V., Zhang, C., Hoffman, J., Bhattacharya, A., Morgan, D., Voyles, P.M., “Imaging of Single La Vacancies in LaMnO3,” preprint, submitted arXiv:06308 (2017).Google Scholar
Ziatdinov, M., Maksov, A., Kalinin, S.V., npj Comp. Mater. 3, 31 (2017).CrossRefGoogle Scholar
Pycroscopy: Scientific Analysis of Nanoscale Materials Imaging Data, https://github.com/pycroscopy/AICrystallographer.Google Scholar
Simonyan, K., Zisserman, A., “Very Deep Convolutional Networks for Large-Scale Image Recognition,” preprint, submitted arXiv:1409.1556 (2014).Google Scholar
https://colab.research.google.com/github/pycroscopy/AICrystallographer/blob/master/Tutorials/Dogs_vs_atoms.ipynbGoogle Scholar
Ziatdinov, M., Dyck, O., Maksov, A., Li, X., Sang, X., Xiao, K., Unocic, R.R., Vasudevan, R., Jesse, S., Kalinin, S.V., ACS Nano 11, 12742 (2017).CrossRefGoogle Scholar
Maksov, A., Dyck, O., Wang, K., Xiao, K., Geohegan, D.B., Sumpter, B.G., Vasudevan, R.K., Jesse, S., Kalinin, S.V., Ziatdinov, M., npj Comp. Mater. 5, 12 (2019).CrossRefGoogle Scholar
Madsen, J., Liu, P., Kling, J., Wagner, J.B., Hansen, T.W., Winther, O., Schiøtz, J., Adv. Theory Simul. 1, 1800037 (2018).CrossRefGoogle Scholar
Rashidi, M., Wolkow, R.A., ACS Nano 12, 5185 (2018).CrossRefGoogle Scholar
Swift, Nion, www.nion.com/swift.Google Scholar
Krivanek, O.L., Chisholm, M.F., Nicolosi, V., Pennycook, T.J., Corbin, G.J., Dellby, N., Murfitt, M.F., Own, C.S., Szilagyi, Z.S., Oxley, M.P., Pantelides, S.T., Pennycook, S.J., Nature 464, 571 (2010).CrossRefGoogle Scholar
Govind Rajan, A., Silmore, K.S., Swett, J., Robertson, A.W., Warner, J.H., Blankschtein, D., Strano, M.S., Nat. Mater. 18, 129 (2019).CrossRefGoogle Scholar
Ziatdinov, M., Dyck, O., Sumpter, B.G., Jesse, S., Vasudevan, R.K., Kalinin, S.V., “Building and Exploring Libraries of Atomic Defects in Graphene: Scanning Transmission Electron and Scanning Tunneling Microscopy Study,” preprint, submitted arXiv:04256 (2018).Google Scholar
Si-Vacancy Complexes in Graphene, https://doi.org/10.25920/0xv3-8459.CrossRefGoogle Scholar
Takagi, H., Takayama, T., Jackeli, G., Khaliullin, G., Nagler, S.E., Nat. Rev. Phys. 1, 264 (2019).CrossRefGoogle Scholar
Borisevich, A.Y., Morozovska, A.N., Kim, Y.M., Leonard, D., Oxley, M.P., Biegalski, M.D., Eliseev, E.A., Kalinin, S.V., Phys. Rev. Lett. 109, 065702 (2012).CrossRefGoogle Scholar
Li, Q., Nelson, C.T., Hsu, S.L., Damodaran, A.R., Li, L.L., Yadav, A.K., McCarter, M., Martin, L.W., Ramesh, R., Kalinin, S.V., Nat. Commun. 8, 1468 (2017).CrossRefGoogle Scholar
Ievlev, A.V., Jesse, S., Cochell, T.J., Unocic, R.R., Protopopescu, V.A., Kalinin, S.V., ACS Nano 9, 11784 (2015).CrossRefGoogle Scholar
Sethna, J.P., Statistical Mechanics: Entropy, Order Parameters and Complexity, 1st ed. (Oxford University Press, Oxford, UK, 2006).Google Scholar
Vlcek, L., Maksov, A., Pan, M.H., Vasudevan, R.K., Kalinin, S.V., ACS Nano 11, 10313 (2017).CrossRefGoogle Scholar
Vlcek, L., Vasudevan, R.K., Jesse, S., Kalinin, S.V., J. Chem. Theory Comput. 13, 5179 (2017).CrossRefGoogle Scholar
Vlcek, L., Ziatdinov, M., Maksov, A., Tselev, A., Baddorf, A.P., Kalinin, S.V., Vasudevan, R.K., ACS Nano 13, 718 (2019).CrossRefGoogle Scholar
Mardt, A., Pasquali, L., Wu, H., Noe, F., Nat. Commun. 9, 5 (2018).CrossRefGoogle Scholar
Kalinin, S.V., Borisevich, A., Jesse, S., Nature 539, 485 (2016).CrossRefGoogle Scholar
Jesse, S., Hudak, B.M., Zarkadoula, E., Song, J., Maksov, A., Fuentes-Cabrera, M., Ganesh, P., Kravchenko, I., Snijders, P.C., Lupini, A.R., Borisevich, A.Y., Kalinin, S.V., Nanotechnology 29, 255303 (2018).CrossRefGoogle Scholar
Richard Feynman′s blackboard at time of his death (1988) Caltech Photo Archives, ID Number 1.10-29.Google Scholar