Abstract
Scanning transmission electron microscopy (STEM) has emerged as a uniquely powerful tool for structural and functional imaging of materials on the atomic level. Driven by advances in aberration correction, STEM now allows the routine imaging of structures with single-digit picometre-level precision for localization of atomic units. This Primer focuses on the opportunities emerging at the interface between STEM and machine learning (ML) methods. We review the primary STEM imaging methods, including structural imaging, electron energy loss spectroscopy and its momentum-resolved modalities and 4D-STEM. We discuss the quantification of STEM structural data as a necessary step towards meaningful ML applications and its analysis in terms of the relevant physics and chemistry. We show examples of the opportunities offered by structural STEM imaging in elucidating the chemistry and physics of complex materials and how the latter connect to first-principles and phase-field models to yield consistent interpretation of generative physics. We present the critical infrastructural needs for the broad adoption of ML methods in the STEM community, including the storage of data and metadata to allow the reproduction of experiments. Finally, we discuss the application of ML to automating experiments and novel scanning modes.
| Original language | English (US) |
|---|---|
| Article number | 11 |
| Journal | Nature Reviews Methods Primers |
| Volume | 2 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2022 |
Funding
This work is based upon work supported by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (S.V.K., M.P.O. and A.R.L.) and was performed and partially supported (M.Z.) at Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a US Department of Energy, Office of Science User Facility. V.G. acknowledges the support of the European Union Horizon 2020 Research and Innovation Programme under grant agreement no. 766970 Q-SORT (H2020-FETOPEN-1-2016-2017), no. 964591 Smart-electrons (H2020-FETOPEN-1-2016-2017) and no. 101035013 MINEON (H2020-FETOPEN-03-2018-2019-2020 – FET Innovation Launchpad). SuperSTEM (D.K.) is the UK National Facility for Advanced Electron Microscopy funded by the Engineering and Physical Sciences Research Council (EPSRC). P.M.V. acknowledges support from DOE BES (DE-FG02-08ER46547). G.G.D.H. and X.L. acknowledge support from Brandeis NSF MRSEC, Bioinspired Soft Materials, DMR-2011846. Work at the Molecular Foundry (C.O.) was supported by the Office of Science, Office of Basic Energy Sciences, of the US Department of Energy under contract no. DE-AC02-05CH11231. E.S. and M.K.Y.C. acknowledges support from the Center for Nanoscale Materials (DOE SUF) under contract no. DE-AC02-06CH11357. C.O. and M.K.Y.C. acknowledge support from DOE Early Career Research Awards. N.S. acknowledges support from the JSPS KAKENHI (grants 20H05659 and 19H05788). J.E. acknowledges the support of the Australian Research Council Discovery Project (grants DP150104483 and DP160104679). The authors are extremely grateful to K. More (from CNMS) for careful reading and editing of the manuscript.
ASJC Scopus subject areas
- General Biochemistry, Genetics and Molecular Biology