Deep learning for electron and scanning probe microscopy

Autor(en)

Sergei Kalinin, Maxim Ziatdinov, Steven R. Spurgeon, Colin Ophus, Eric A. Stach, Toma Susi, Josh Agar, John Randall

Abstrakt

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment in imaging. In this article, we discuss recent progress in application of machine learning methods in scanning transmission electron microscopy and scanning probe microscopy, from applications such as data compression and exploratory data analysis to physics learning to atomic fabrication.

Organisation(en)

Physik Nanostrukturierter Materialien

Externe Organisation(en)

University of Tennessee at Knoxville, Oak Ridge National Laboratory , Pacific Northwest National Laboratory, University of Washington, Lawrence Berkeley National Laboratory, University of Pennsylvania, Drexel University, Zyvex Labs

Journal

MRS Bulletin

Band

47

Seiten

931-939

Anzahl der Seiten

9

ISSN

0883-7694

DOI

https://doi.org/10.1557/s43577-022-00413-3

Publikationsdatum

09-2022

Peer-reviewed

Ja

ÖFOS 2012

103018 Materialphysik

Schlagwörter

ASJC Scopus Sachgebiete

Condensed Matter Physics, Allgemeine Materialwissenschaften, Physical and Theoretical Chemistry

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/9b35902b-eb39-4059-88f8-ca5b55f52b94