Journal article
Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication
MRS BULLETIN, v 47(9), p931
Sep 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Machine learning and artificial intelligence (MUAI) 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 MUAI 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.
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Details
- Title
- Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication
- Publication Details
- MRS BULLETIN, v 47(9), p931
- Publisher
- SPRINGER HEIDELBERG; HEIDELBERG
- Grant note
- This effort (S.V.K.) is based upon work supported by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division, and was performed and partially supported (M.Z.) at the Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a US Department of Energy, Office of Science User Facility. S.R.S. was supported by the Energy Storage Materials Initiative (ESMI), under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for the US Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830. Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the US Department of Energy under Contract No. DE-AC02-05CH11231. This material (J.C.A.) is based upon work supported by the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award No. DE-SC-0002501, and the US Army Research Laboratory under Grant No. W911NF-19-2-0119. T.S. was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 756277-ATMEN). E.A.S. was supported by the National Science Foundation through the University of Pennsylvania Materials Research Science and Engineering Center (MRSEC) (DMR-1720530). The work at Zyvex Labs is based upon work supported by the US Department of Energy Office of Energy Efficiency and Renewable Energy (EERE) under Advanced Manufacturing Office Award No. DEEE0008311.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000878474500003
- Scopus ID
- 2-s2.0-85141188942
- Other Identifier
- 991021861308704721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Materials Science, Multidisciplinary
- Physics, Applied