Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication
- Author(s)
- Sergei Kalinin, Maxim Ziatdinov, Steven R. Spurgeon, Colin Ophus, Eric A. Stach, Toma Susi, Josh Agar, John Randall
- Abstract
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(s)
- Physics of Nanostructured Materials
- External organisation(s)
- 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
- Volume
- 47
- Pages
- 931-939
- No. of pages
- 9
- ISSN
- 0883-7694
- DOI
- https://doi.org/10.1557/s43577-022-00413-3
- Publication date
- 09-2022
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103018 Materials physics
- Keywords
- ASJC Scopus subject areas
- Condensed Matter Physics, Materials Science(all), Physical and Theoretical Chemistry
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/deep-learning-for-electron-and-scanning-probe-microscopy-from-materials-design-to-atomic-fabrication(9b35902b-eb39-4059-88f8-ca5b55f52b94).html