Issue 12, 2022

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

Abstract

In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.

Graphical abstract: Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

Article information

Article type
Review Article
Submitted
10 rugp. 2022
Accepted
12 rugs. 2022
First published
14 spal. 2022
This article is Open Access
Creative Commons BY license

Nanoscale Horiz., 2022,7, 1427-1477

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

M. Botifoll, I. Pinto-Huguet and J. Arbiol, Nanoscale Horiz., 2022, 7, 1427 DOI: 10.1039/D2NH00377E

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