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Machine Learning-Driven Automated Scanning Probe Microscopy for Ferroelectrics

Published online by Cambridge University Press:  22 July 2022

Yongtao Liu*
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Kyle P. Kelley
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Rama K. Vasudevan
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Hiroshi Funakubo
Affiliation:
Department of Material Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan
Shelby S. Fields
Affiliation:
Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA, USA
Takanori Mimura
Affiliation:
Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA, USA
Susan Trolier-McKinstry
Affiliation:
Center for Dielectrics and Piezoelectrics, Materials Research Institute, The Pennsylvania State University, University Park, PA, USA Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, USA
Jon F. Ihlefeld
Affiliation:
Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA, USA Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA
Maxim Ziatdinov
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Sergei V. Kalinin
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
*
*Corresponding author: liuy3@ornl.gov

Abstract

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Type
Microscopy Infrastructures: Architectures, Avenues and Access
Copyright
Copyright © Microscopy Society of America 2022

References

Liu, Y et al. , Advanced Materials, 33 no.43 (2021), 2103680.CrossRefGoogle Scholar
Liu, Y et al. , Nature Machine Intelligence, accepted (2022).Google Scholar
Liu, Y et al. , arXiv preprint, arXiv:2202.01089 (2022).Google Scholar
Liu, Y et al. , arXiv preprint arXiv:2111.09918 (2021).Google Scholar
This work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Energy Frontier Research Centers program under Award Number DE-SC0021118. This work is conducted at the Center for Nanophase Materials Sciences, a US Department of Energy Office of Science User Facility.Google Scholar