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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
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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

Information

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 

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Footnotes

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

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