Machine learning in electron microscopy for advanced nanocharacterization : current developments, available tools and future outlook
Botifoll, Marc (Institut Català de Nanociència i Nanotecnologia)
Pinto-Huguet, Ivan (Institut Català de Nanociència i Nanotecnologia)
Arbiol i Cobos, Jordi (Institut Català de Nanociència i Nanotecnologia)

Data:	2022
Resum:	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.
Ajuts:	European Commission 101094299 Ministerio de Ciencia e Innovación PID2020-116093RB-C43 Ministerio de Economía y Competitividad SEV-2017-0706 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-327
Nota:	Altres ajuts: ICN2 is funded by the CERCA Programme/Generalitat de Catalunya. Part of the present work has been performed within the framework of the Universitat Autònoma de Barcelona, Materials Science PhD programme. We acknowledge support from CSIC Interdisciplinary Thematic Platform (PTI+) on Quantum Technologies (PTQTEP+). M. B. acknowledges funding from SUR Generalitat de Catalunya and the EU Social Fund; project ref. 2020 FI 00103.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Exploration and development ; High dimensionality ; Machine-learning ; Material science ; Nano science ; Nano-characterization ; New high ; State of the art ; Work-flows ; Artificial Intelligence ; Automation ; Machine Learning ; Microscopy, Electron ; Robotics
Publicat a:	Nanoscale horizons, Vol. 7, issue 12 (Dec. 2022) , p. 1427-1477, ISSN 2055-6764

DOI: 10.1039/d2nh00377e

51 p, 6.4 MB

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